├── .gitignore └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | filter_papers.ipynb 2 | fl_dblp_* 3 | filter_keywords.py 4 | papers_fl.csv 5 | papers_fl.md 6 | README_bk.md -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Table of Contents 2 | 1. [Federated Learning Papers](#federated-learning-papers) 3 | 2. [Other Research Topics](#other-research-topics) 4 | 3. [Federated Learning Papers with Code](#federated-learning-papers-with-code) 5 | 4. [Data Sources](#data-sources) 6 | 5. [Contributing](#contributing) 7 | 6. [Support](#support) 8 | 9 | ## Federated Learning Papers 10 | This GitHub repository contains an updated list of Federated Learning papers as of **December 18, 2025**. 11 | 12 | ### Overview 13 | - **Total Papers**: Updated regularly with latest publications 14 | - **Coverage**: Papers from 2016 to present 15 | - **Sources**: Collected from arXiv, NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, IJCAI, KDD, CVPR, ICCV, ECCV, IEEE, ACM, Springer, ScienceDirect, Nature, and other top AI/ML conferences and journals 16 | - **Interactive Search**: For a better reading experience, visit the [Shinyapps website](https://mtuann.shinyapps.io/research-papers/) 17 | 18 | ### Key Features 19 | - 📊 **Comprehensive Coverage**: Papers from major AI/ML venues 20 | - 🔍 **Advanced Search**: Filter by title, author, venue, year 21 | - 📅 **Regular Updates**: Automated collection of new papers 22 | - 💻 **Code Availability**: Identifies papers with available code 23 | - 📈 **Trending Research**: Focus on cutting-edge developments 24 | 25 | --- 26 | 27 | ## Other Research Topics 28 | Explore additional research papers on the following topics: 29 | 30 | ### Machine Learning & AI 31 | - **[Large Language Models](https://github.com/mtuann/llm-updated-papers)** - LLM research and applications 32 | - **[Federated Learning](https://github.com/mtuann/federated-learning-updated-papers)** - Distributed machine learning 33 | - **[Backdoor Learning](https://github.com/mtuann/backdoor-ai-resources)** - Adversarial machine learning 34 | - **[Machine Unlearning](https://github.com/mtuann/machine-unlearning-papers)** - Data removal and privacy 35 | 36 | ### Computing & Systems 37 | - **[Serverless Computing](https://mtuann.shinyapps.io/research-papers/)** - Cloud computing architectures 38 | - **[Multi-Modal Learning](https://mtuann.shinyapps.io/research-papers/)** - Multi-modal AI systems 39 | 40 | ### Interactive Platforms 41 | - **[Research Papers App](https://mtuann.shinyapps.io/research-papers/)** - Search and explore all papers 42 | - **[Paper Collections](https://github.com/mtuann/research-papers)** - Main repository with all datasets 43 | 44 | --- 45 | 46 | ## Data Sources 47 | The papers are collected from the following sources: 48 | 49 | ### Academic Databases 50 | - **arXiv** (1991-present) - Preprints and published papers 51 | - **OpenReview** - Conference submissions and peer reviews 52 | - **ACM Digital Library** - Computer science publications 53 | - **Springer** - Academic journals and conferences 54 | - **ScienceDirect** - Elsevier publications 55 | - **Nature** - High-impact research papers 56 | - **DBLP** - Computer science bibliography 57 | - **Google Scholar** - Academic search engine 58 | - **CrossRef** - DOI registration agency 59 | - **OpenAlex** - Open scholarly data 60 | 61 | ### Major Conferences & Journals 62 | - **Machine Learning**: NeurIPS, ICML, ICLR, JMLR, TMLR 63 | - **Natural Language Processing**: ACL, EMNLP, NAACL, COLING 64 | - **Computer Vision**: CVPR, ICCV, ECCV, PAMI, IJCV 65 | - **Artificial Intelligence**: AAAI, IJCAI, AAMAS 66 | - **Data Mining**: KDD, ICDM, SDM, TKDD 67 | - **Security & Privacy**: CCS, USENIX Security, NDSS 68 | - **And many more...** 69 | 70 | --- 71 | 72 | ## Federated Learning Papers with Code 73 | Due to GitHub repository limitations, this section includes only those papers that provide accompanying code, sorted by publication date. For access to the full list of papers, please visit the [Shinyapps website](https://mtuann.shinyapps.io/research-papers/). 74 | 75 | 87 | 88 | --- 89 | 90 | ## Contributing 91 | We welcome contributions to improve this paper collection: 92 | 93 | ### How to Contribute 94 | 1. **Add Missing Papers**: Submit papers that should be included 95 | 2. **Improve Metadata**: Help enhance paper information 96 | 3. **Report Issues**: Identify bugs or missing features 97 | 4. **Suggest Improvements**: Propose new features or enhancements 98 | 99 | ### Contact Information 100 | - **Email**: [tuannm0312@gmail.com](mailto:tuannm0312@gmail.com) 101 | - **GitHub Issues**: [Create an issue](https://github.com/mtuann/research-papers/issues) 102 | - **Discussions**: [Join the discussion](https://github.com/mtuann/research-papers/discussions) 103 | 104 | --- 105 | 106 | ## Support 107 | If you find this application helpful and would like to support its development, you can buy me a coffee using one of the following methods: 108 | 109 | ### Payment Methods 110 | - **Techcombank (Vietnam)**: 5877 5555 55 (Nguyen Thi Lan Phuong) 111 | - **PayPal or Credit/Debit Card**: [https://ko-fi.com/miutheladycat](https://ko-fi.com/miutheladycat) 112 | 113 | ### Why Support? 114 | Your support helps maintain and improve: 115 | - 🤖 Automated paper collection pipeline 116 | - 🌐 Interactive web application 117 | - 📊 Regular data updates 118 | - 🔧 System maintenance and improvements 119 | - 📚 New research area coverage 120 | 121 | --- 122 | 123 | **Note**: This repository is regularly updated with new papers. For the most current data, check the [Shinyapps website](https://mtuann.shinyapps.io/research-papers/) or the individual topic repositories linked above. 124 | 125 | 126 | |No.|Title|Authors|Publish Date|Venue|Code|URL| 127 | |---|---|---|---|---|---|---| 128 | |1|Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication|Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/sparse-binary-compression-towards-distributed/code)|https://openreview.net/pdf/79a831ab8097889e3fd0194e2ca435da6c069550.pdf| 129 | |2|Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training|Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/deep-gradient-compression-reducing-the/code)|https://openreview.net/pdf/41772454cc4bd99cc9865acd9eb52dadf67ccb50.pdf| 130 | |3|Shuffle Gaussian Mechanism for Differential Privacy|Seng Pei Liew, Tsubasa Takahashi||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 3 code implementations](https://www.catalyzex.com/paper/shuffle-gaussian-mechanism-for-differential/code)|https://openreview.net/pdf/6a177e06ea96d826fdc4e3225b1f5421dc808586.pdf| 131 | |4|Low Rank Training of Deep Neural Networks for Emerging Memory Technology|Albert Gural, Phillip Nadeau, Mehul Tikekar, Boris Murmann||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/low-rank-training-of-deep-neural-networks-for/code)|https://openreview.net/pdf/ded4869c017f6803939dcc2ebf18c9cca5342392.pdf| 132 | |5|Learning Differentially Private Recurrent Language Models|H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/learning-differentially-private-recurrent/code)|https://openreview.net/pdf/3f8fd2b61e7e83c63a36b191a9a9881f9a8602e6.pdf| 133 | |6|Federated causal discovery|Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 4 code implementations](https://www.catalyzex.com/paper/federated-causal-discovery/code)|https://openreview.net/pdf/5147d31a2ed490c32efc78d17a12d562b54d11a0.pdf| 134 | |7|Federated Distillation of Natural Language Understanding with Confident Sinkhorns|Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 5 code implementations](https://www.catalyzex.com/paper/federated-distillation-of-natural-language/code)|https://openreview.net/pdf/0cfb9ce546c1723a5c53d1c63a403cf301d57cf8.pdf| 135 | |8|Dynamic Differential-Privacy Preserving SGD|Jian Du, Song Li, Fengran Mo, Siheng Chen||OpenReview|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/dynamic-differential-privacy-preserving-sgd/code)|https://openreview.net/pdf/8bf56ae024baac064694c15c86813ea02f0b9c02.pdf| 136 | |9|Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks|Elmahallawy, Mohamed, Akbarfam, Asma Jodeiri|2025-12-09|arXiv (Cornell University)|https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git|https://doi.org/10.48550/arxiv.2512.08882| 137 | |10|The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning|Linardos, Akis, Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Foley, Patrick, Ta, Kevin, Chung, Verena, Sheller, Micah,...|2025-12-05|arXiv (Cornell University)|https://github.com/FeTS-AI/Challenge.|https://doi.org/10.48550/arxiv.2512.06206| 138 | |11|A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization|Li, Tianle, Huang Yongzhi, Jiang Linshan, Liu Chang, Xie Qi-peng, Du Wenfeng, Wang Lu, Wu, Kaishun|2025-11-27|arXiv (Cornell University)|https://github.com/Huang-Yongzhi/NeurlPS_FedWMSAM.|https://doi.org/10.48550/arxiv.2511.22080| 139 | |12|FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning|Yao, Yuan, Wang Li-xu, Wu, Jiaqi, Song Jin, Chen Si-min, Wang ZeHua, Tian Zijian, Chen, Wei, Li Huixia, Li, Xiaoxiao|2025-11-27|arXiv (Cornell University)|https://github.com/AIResearch-Group/FedRE.|https://doi.org/10.48550/arxiv.2511.22265| 140 | |13|\textitFLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning|Younesi, Abolfazl, Kiss, Leon, Samani, Zahra Najafabadi, Poveda, Juan Aznar, Fahringer, Thomas|2025-11-18|arXiv (Cornell University)|https://github.com/Anonymous0-0paper/FLARE|http://arxiv.org/abs/2511.14715| 141 | |14|FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning|Younesi, Abolfazl, Kiss, Leon, Samani, Zahra Najafabadi, Poveda, Juan Aznar, Fahringer, Thomas|2025-11-18|arXiv (Cornell University)|https://github.com/Anonymous0-0paper/FLARE|https://doi.org/10.48550/arxiv.2511.14715| 142 | |15|Optimizing Quantum Federated Learning: Addressing non-IID data challenges with global data sharing in weighted model averaging and clustering-based parameter selection|Zhiqiang Guo, Jianbin Hu, Wenyu Yang, Yunqian Wang|2025-11-14|Physica Scripta|https://github.com/wang-lab-repository/qavg-gzq.git.|https://doi.org/10.1088/1402-4896/ae1fb3| 143 | |16|Curriculum Guided Personalized Subgraph Federated Learning|M.-S. Kang, Hogun Park|2025-11-08|OpenAlex|https://github.com/Kang-Min-Ku/CUFL.git.|https://doi.org/10.48550/arXiv.2509.00402| 144 | |17|FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts|Wang Wei Bo, Yanpeng Sun, Yu Wang, Xinyu Zhang, Zechao Li|2025-11-01|arXiv|https://github.com/weihao-bo/FedMGP.git.|https://doi.org/10.48550/arXiv.2511.00480| 145 | |18|DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm|Junkang Liu, Yuxuan Tian, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Junchao Zhou, Ding Daorui|2025-10-31|arXiv|https://github.com/junkangLiu0/DP-FedPGN|https://doi.org/10.48550/arXiv.2510.27504| 146 | |19|FedDPGu: Adaptive Prompt-tuning with Built-in Unlearning for Federated Learning|Lishan Yang, Wei Emma Zhang, Ali Shakeri, Amin Beheshti, Weitong Chen, Jian Yang|2025-10-30|OpenAlex|https://github.com/gotobcn8/FedDPG.|https://doi.org/10.21203/rs.3.rs-7488594/v1| 147 | |20|Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility|Kangkang Sun, Jiayuan Wu, Minyi Guo, Jianhua Li, Jianwei Huang|2025-10-30|arXiv|https://github.com/szpsunkk/FedPF.|https://doi.org/10.48550/arXiv.2510.26841| 148 | |21|FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning|Zhiqin Yang, Yonggang Zhang, Chenxin Li, Yiu-ming Cheung, Bo Han, Yixuan Yuan|2025-10-23|arXiv|https://github.com/CUHK-AIM-Group/FedGPS.|https://doi.org/10.48550/arXiv.2510.20250| 149 | |22|CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification|L. K. Li, Junbin Mao, Hanhe Lin, Xu Tian, Fang‐Xiang Wu, Jin Liu|2025-10-20|arXiv|https://github.com/LD0416/CEPerFed.|https://doi.org/10.48550/arXiv.2510.17584| 150 | |23|RAIM: three-stage stackelberg game for hierarchical federated learning with reputation-aware incentive mechanism|Cuihua Zuo, Peihua Xu, Yong Song, Jianfeng Lu, Yuan Cao, Yuanman Li|2025-10-02|Research Square (Research Square)|https://github.com/Sensorjang/RAIM_FedML_experiment_ZCH-master.|https://doi.org/10.21203/rs.3.rs-6548264/v1| 151 | |24|GradCAM-AE: A New Shield Defense against Poisoning Attacks on Federated Learning|Jingjing Zheng, Kai Li, Xin Yuan, Wei Ni, Eduardo Tovar, Özgür B. Akan|2025-09-12|ACM Transactions on Privacy and Security|https://github.com/jjzgeeks/GradCAM-AE|https://doi.org/10.1145/3765743| 152 | |25|PPFL: A Personalized Federated Learning Framework for Heterogeneous Population|Di Hao, Yi Yang, H. Ye, Xiangyu Chang|2025-08-25|INFORMS journal on computing|https://github.com/INFORMSJoC/2023.0376|https://doi.org/10.1287/ijoc.2023.0376| 153 | |26|Privacy‐Preserving Crowd Counting via Quantum‐Enhanced Federated Learning|Chen Zhang, Jing’an Cheng, Qiang Zhou, Wenzhe Zhai, Mingliang Gao|2025-07-28|Expert Systems|https://github.com/sdutzhangchen/PQNet|https://doi.org/10.1111/exsy.70098| 154 | |27|Privacy Protection and Statistical Efficiency Trade-Off for Federated Learning|Haobo Qi, Feifei Wang, Hansheng Wang|2025-07-15|INFORMS journal on computing|https://github.com/INFORMSJoC/2024.0554|https://doi.org/10.1287/ijoc.2024.0554| 155 | |28|Personalized Multi-tier Federated Learning|Sourasekhar Banerjee, Ali Dadras, Alp Yurtsever, Monowar H. Bhuyan|2025-07-06|Communications in computer and information science|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/personalized-multi-tier-federated-learning/code)|https://openreview.net/pdf/b5ccc09a1be75dd37e199cda4374ab68fa873ab2.pdf| 156 | |29|pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models|Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani|2025-07-01|arXiv|https://github.com/sajjad-ucsb/pFedMMA.|http://arxiv.org/abs/2507.05394v1| 157 | |30|S2FGL: Spatial Spectral Federated Graph Learning|Zihan Tan, Suyuan Huang, Guancheng Wan, Wenke Huang, He Li, Mang Ye|2025-07-01|arXiv|https://github.com/Wonder7racer/S2FGL.git.|http://arxiv.org/abs/2507.02409v2| 158 | |31|Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing|Zhufeng Lu, Chentao Jia, Ming Hu, Xiaofei Xie, Mingsong Chen|2025-07-01|arXiv|https://github.com/mastlab-T3S/FedRAS.|http://arxiv.org/abs/2507.08842v1| 159 | |32|Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection|Rei Tamaru, Pei Li, Bin Ran|2025-07-01|arXiv|https://github.com/raynbowy23/FedMeta-GeoLane.git.|http://arxiv.org/abs/2507.08743v1| 160 | |33|BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning|Thinh Dao, Dung Thuy Nguyen, Khoa D. Doan, Kok-Seng Wong|2025-07-01|arXiv|https://github.com/thinh-dao/BackFed.|https://doi.org/10.48550/arXiv.2507.04903| 161 | |34|Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity|Qiao Xiao, Boqian Wu, Andrey Poddubnyy, Elena Mocanu, Phuong H. Nguyen, Mykola Pechenizkiy, Decebal Constantin Mocanu|2025-06-01|arXiv|https://github.com/QiaoXiao7282/LIPS.|https://doi.org/10.48550/arXiv.2506.00932| 162 | |35|Federated ADMM from Bayesian Duality|Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez, Mohammad Emtiyaz Khan|2025-06-01|arXiv|https://github.com/team-approx-bayes/bayes-admm|http://arxiv.org/abs/2506.13150v1| 163 | |36|FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation|Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni|2025-06-01|arXiv|https://github.com/siomvas/FedCLAM.|http://arxiv.org/abs/2506.22580v1| 164 | |37|FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model|Md Jueal Mia, M. Hadi Amini|2025-06-01|arXiv|https://github.com/solidlabnetwork/fedshield-llm|http://arxiv.org/abs/2506.05640v1| 165 | |38|Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM|Xun Li, Qiong Wu, Pingyi Fan, Kezhi Wang, Nan Cheng, Khaled B. Letaief|2025-06-01|IEEE Networking Letters|https://github.com/qiongwu86/Federated-Learning-Assisted-Edge-Caching-Scheme-Based-on-Lightweight-Architecture-DDPM|https://doi.org/10.48550/arXiv.2506.04593| 166 | |39|HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark|Jianqing Zhang, Xinghao Wu, Yanbing Zhou, Xiaoting Sun, Qiqi Cai, Yang Liu, Yang Hua, Zhenzhe Zheng, Jian Cao, Qiang Yan...|2025-06-01|OpenAlex|https://github.com/TsingZ0/HtFLlib.|https://doi.org/10.48550/arXiv.2506.03954| 167 | |40|Latency Optimization for Wireless Federated Learning in Multihop Networks|Shaba Shaon, Van-Dinh Nguyen, Dinh C. Nguyen|2025-06-01|IEEE Transactions on Vehicular Technology|https://github.com/ShabaGit/Multihop_FL|https://doi.org/10.48550/arXiv.2506.12081| 168 | |41|Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning|Jiahui Wu, Tiecheng Sun, Fucai Luo, Haiyan Wang, Weizhe Zhang|2025-06-01|arXiv|https://github.com/JiahuiWu2022/SMHE.git.|https://doi.org/10.48550/arXiv.2506.20101| 169 | |42|UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data|Sunny Gupta, Nikita Jangid, Amit Sethi|2025-06-01|arXiv|https://github.com/sunnyinAI/UniVarFL|https://doi.org/10.48550/arXiv.2506.08167| 170 | |43|Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments|Junming Liu, Yanting Gao, Siyuan Meng, Yifei Sun, Aoqi Wu, Yufei Jin, Yirong Chen, Ding Wang, Guosun Zeng|2025-05-01|arXiv|https://github.com/Wings-Of-Disaster/Mosaic.|http://arxiv.org/abs/2505.19699v1| 171 | |44|A Federated Random Forest Solution for Secure Distributed Machine Learning|Alexandre Cotorobai, Jorge Miguel Silva, Jose Luis Oliveira|2025-05-01|arXiv|https://github.com/ieeta-pt/fed_rf.|http://arxiv.org/abs/2505.08085v1| 172 | |45|DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries|Abhijit Talluri|2025-05-01|arXiv|https://github.com/abhitall/federated-credit-risk-rtfl.git|https://doi.org/10.48550/arXiv.2505.23813| 173 | |46|Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach|Hongqing Pan, Yanjun Zhang, Leo Yu Zhang, Scott D. Adams, Abbas Z. Kouzani, Suiyang Khoo|2025-05-01|OpenAlex|https://github.com/Halsey777/FedSA|https://doi.org/10.48550/arXiv.2505.16403| 174 | |47|Multimodal Federated Learning With Missing Modalities through Feature Imputation Network|Pranav Poudel, Aavash Chhetri, Prashnna K. Gyawali, Georgios Leontidis, Binod Bhattarai|2025-05-01|Lecture notes in computer science|https://github.com/bhattarailab/FedFeatGen|https://doi.org/10.1007/978-3-031-98688-8_20| 175 | |48|The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning|Shiwei Li, Xiandi Luo, Haozhao Wang, Xing Tang, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li|2025-05-01|arXiv|https://github.com/Leopold1423/fedmud-icml25.|https://openreview.net/forum?id=aooq3tQIX9| 176 | |49|Unlearning for Federated Online Learning to Rank: A Reproducibility Study|Yiling Tao, Shuyi Wang, Jiaxi Yang, Guido Zuccon|2025-05-01|arXiv|https://github.com/Iris1026/Unlearning-for-FOLTR.git.|http://arxiv.org/abs/2505.12791v1| 177 | |50|Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning|Mengmeng Chen, Xiaohu Wu, Qiqi Liu, Tiantian He, Yew-Soon Ong, Yaochu Jin, Qicheng Lao, Han Yu|2025-05-01|arXiv|https://github.com/buptcmm/phnhvvs|https://openreview.net/forum?id=hrBfufwMzg| 178 | |51|FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments|Duy-Dong Le, Nguyen Huynh Tuong, Tran Anh Khoa, Minh-Son Dao, Pham The Bao|2025-04-04|bioRxiv (Cold Spring Harbor Laboratory)|https://github.com/dongld-2020/fednolowe|https://doi.org/10.1101/2025.03.30.646222| 179 | |52|Federated Learning for the pathogenicity annotation of genetic variants in multi-site clinical settings|Nigreisy Montalvo, Francisco Requena Silvente, Emidio Capriotti, Antonio Rausell|2025-04-04|medRxiv (Cold Spring Harbor Laboratory)|https://github.com/RausellLab/FedLearnVar.|https://doi.org/10.1093/bioinformatics/btaf523| 180 | |53|The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning|Eleanor Wallach, Sage Siler, Jing Deng|2025-04-01|arXiv|https://github.com/Eleanor-W/KCI_for_FL.|https://doi.org/10.48550/arXiv.2504.08198| 181 | |54|mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup|Xuan-Hao Liu, Bao-Liang Lu, Wei-Long Zheng|2025-04-01|arXiv|https://github.com/XuanhaoLiu/mixEEG.|https://doi.org/10.48550/arXiv.2504.07987| 182 | |55|Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation|Kitsuya Azuma, Takayuki Nishio, Yuichi Kitagawa, Wakako Nakano, Takahito Tanimura|2025-04-01|arXiv|https://github.com/kitsuyaazuma/SCARLET.|http://arxiv.org/abs/2504.19602v2| 183 | |56|Achieving Distributive Justice in Federated Learning via Uncertainty Quantification|Alycia N. Carey, Xintao Wu|2025-04-01|arXiv|https://github.com/alycia-noel/UDJ-FL.|https://doi.org/10.48550/arXiv.2504.15924| 184 | |57|Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs|Kishan Gurumurthy, Himanshu Pal, Charu Sharma|2025-04-01|arXiv|https://github.com/SpringWiz11/Fed-GNODEFormer|http://arxiv.org/abs/2504.11808v1| 185 | |58|Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization|Shuai Gong, Chaoran Cui, Xiaolin Dong, Xiushan Nie, Lei Zhu, Xiaojun Chang|2025-04-01|arXiv|https://github.com/GongShuai8210/TRIP.|http://arxiv.org/abs/2504.21063v1| 186 | |59|A Study on the Efficiency of Combined Reconstruction and Poisoning Attacks in Federated Learning|Christian Becker, José Antonio Peregrina, Frauke Beccard, Marisa Mohr, Christian Zirpins|2025-03-20|Journal of Data Science and Intelligent Systems|https://github.com/zalandoresearch/fashion-mnist.|https://doi.org/10.47852/bonviewjdsis52023970| 187 | |60|Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions|Kasra Borazjani, Payam Abdisarabshali, Naji Khosravan, Seyyedali Hosseinalipour|2025-03-01|arXiv|https://github.com/KasraBorazjani/task-perspective-het|https://doi.org/10.48550/arXiv.2503.14553| 188 | |61|UltraFlwr -- An Efficient Federated Medical and Surgical Object Detection Framework|Yang Li, Soumya Snigdha Kundu, Maxence Boels, Toktam Mahmoodi, Sebastien Ourselin, Tom Vercauteren, Prokar Dasgupta, Jon...|2025-03-01|arXiv|https://github.com/KCL-BMEIS/UltraFlwr.|http://arxiv.org/abs/2503.15161v1| 189 | |62|TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models|Caspar Meijer, Jiyue Huang, Shreshtha Sharma, Elena Lazovik, Lydia Y. Chen|2025-03-01|arXiv|https://github.com/Capsar/ts-inverse|http://arxiv.org/abs/2503.20952v1| 190 | |63|Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients|Xiuwen Fang, Mang Ye, Bo Du|2025-03-01||https://github.com/FangXiuwen/RAHFL.|https://doi.org/10.1109/TPAMI.2025.3527137| 191 | |64|Federated nnU-Net for Privacy-Preserving Medical Image Segmentation|Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla, Lidia Garrucho, Dimitri A. Kessler, Esmeralda Ruiz Pujadas, O...|2025-03-01|arXiv|https://github.com/faildeny/FednnUNet|http://arxiv.org/abs/2503.02549v1| 192 | |65|Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch|Yijie Liu, Xinyi Shang, Yiqun Zhang, Yang Lu, Chen Gong, Jing-Hao Xue, Hanzi Wang|2025-03-01|arXiv|https://github.com/Jay-Codeman/SAGE|http://arxiv.org/abs/2503.13227v1| 193 | |66|Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning|Yanbiao Ma, Wei Dai, Wenke Huang, Jiayi Chen|2025-03-01|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|https://github.com/WeiDai-David/2025CVPR_GGEUR|https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html| 194 | |67|Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation|Ziang Lu, Lei Guo, Xu Yu, Zhiyong Cheng, Xiaohui Han, Lei Zhu|2025-03-01|arXiv|https://github.com/Sapphire-star/FFMSR.|http://arxiv.org/abs/2503.23026v1| 195 | |68|FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution|Ali Mollaahmadi Dehaghi, Hossein KhademSohi, Reza Razavi, Steve Drew, Mohammad Moshirpour|2025-03-01|arXiv|https://github.com/alimd94/FedVSR|https://doi.org/10.48550/arXiv.2503.13745| 196 | |69|Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching|Nannan Wu, Zhuo Kuang, Zengqiang Yan, Ping Wang, Li Yu|2025-03-01|arXiv|https://github.com/wnn2000/FFL4MIA.|http://arxiv.org/abs/2503.09587v1| 197 | |70|FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning|Dongping Liao, Xitong Gao, Yabo Xu, Chengzhong Xu|2025-03-01|arXiv|https://github.com/0-ml/flip|http://arxiv.org/abs/2503.22263v1| 198 | |71|Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning|Xiaoming Qi, Jingyang Zhang, Huazhu Fu, Guanyu Yang, Shuo Li, Yueming Jin|2025-03-01|Information Processing in Medical Imaging(IPMI)2025|https://github.com/jinlab-imvr/FedDAH.|http://arxiv.org/abs/2503.20808v1| 199 | |72|Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection|Jiahao Xu, Zikai Zhang, Rui Hu|2025-03-01|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|https://github.com/JiiahaoXU/AlignIns.|https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html| 200 | |73|Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning|Jingyun Chen, Yading Yuan|2025-03-01|arXiv|https://github.com/CUMC-Yuan-Lab/GCML|http://arxiv.org/abs/2503.03883v2| 201 | |74|BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning|Yu Zhou, Bingyan Liu|2025-03-01|OpenAlex|https://github.com/ZhouYuCS/BTFL|https://doi.org/10.48550/arXiv.2503.06633| 202 | |75|A Survey on Federated Fine-tuning of Large Language Models|Yebo Wu, Chunlin Tian, Jingguang Li, He Sun, Kahou Tam, Zhanting Zhou, Haicheng Liao, Zhijiang Guo, Li Li, Chengzhong Xu|2025-03-01|arXiv|https://github.com/Clin0212/Awesome-Federated-LLM-Learning|http://arxiv.org/abs/2503.12016v2| 203 | |76|FAA-CLIP: Federated Adversarial Adaptation of CLIP|Yihang Wu, Ahmad Chaddad, Christian Desrosiers, Tareef Daqqaq, Reem Kateb|2025-03-01|arXiv|https://github.com/AIPMLab/FAA-CLIP.|http://arxiv.org/abs/2503.05776v1| 204 | |77|FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data|Yuxia Sun, Ang Sun, Shin-Ying Pan, Zhixiao Fu, Jingcai Guo|2025-02-11|OpenAlex|https://github.com/Yuxia-Sun/FL_FedAPA.|https://doi.org/10.48550/arXiv.2502.07456| 205 | |78|PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning|Yu Feng, Yangli-ao Geng, Yifan Zhu, Zongfu Han, Xie Yu, Kaiwen Xue, Haoran Luo, Mengyang Sun, Guangwei Zhang, Meina Song|2025-02-01|OpenAlex|https://github.com/dannis97500/PM-MOE|https://doi.org/10.48550/arXiv.2502.00354| 206 | |79|Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning|Linian Wang, Leye Wang|2025-02-01|arXiv|https://github.com/wangln19/vertical-federated-unlearning.|https://doi.org/10.48550/arXiv.2502.17081| 207 | |80|FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning|Meilu Zhu, Qiushi Yang, Zhifan Gao, Yixuan Yuan, Jun Liu|2025-02-01|Medical Image Analysis|https://github.com/CUHK-AIM-Group/FedBM.|https://doi.org/10.1016/j.media.2025.103524| 208 | |81|Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning|Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Lav R. Varshney, Praneeth Vepakomma|2025-02-01|arXiv|https://github.com/CERT-Lab/fed-sb.|http://arxiv.org/abs/2502.15436v1| 209 | |82|A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing|Guannan Lai, Yihui Feng, Xin Yang, Xiaoyu Deng, Hao Yu, Shuyin Xia, Guoyin Wang, Tianrui Li|2025-01-08|arXiv|https://github.com/AIGNLAI/GrBFL.|https://doi.org/10.48550/arXiv.2501.04940| 210 | |83|Balanced coarse-to-fine federated learning for noisy heterogeneous clients|Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang|2025-01-07|Complex & Intelligent Systems|https://github.com/drafly/bcffl.|https://doi.org/10.1007/s40747-024-01694-8| 211 | |84|Stones From Other Hills: Intrusion Detection in Statistical Heterogeneous IoT by Self-Labeled Personalized Federated Learning|Wenting Lu, Ayong Ye, Peixin Xiao, Yuanhuang Liu, Longjing Yang, Donglin Zhu, Zhiquan Liu|2025-01-01|IEEE Internet of Things Journal|https://github.com/deer-echo/SOH-FL.git.|https://doi.org/10.1109/JIOT.2025.3526379| 212 | |85|Optimized Local Updates in Federated Learning via Reinforcement Learning|Ali Murad, Bo Hui, Wei-Shinn Ku|2025-01-01|IJCNN|https://github.com/amuraddd/optimized_client_training.git.|https://doi.org/10.1109/IJCNN64981.2025.11227867| 213 | |86|Optimizing Communication Efficiency through Training Potential in Multi-Modal Federated Learning|Yinghao Zhang, Jianxiong Guo, Xingjian Ding, Zhiqing Tang, Tian Wang, Weili Wu, Weijia Jia|2025-01-01|ACM Transactions on Internet Technology|https://github.com/1643204431/OCETPMMFL.|https://doi.org/10.1145/3747590| 214 | |87|Owen Sampling Accelerates Contribution Estimation in Federated Learning|Hossein KhademSohi, Hadi Hemmati, Jiayu Zhou, Steve Drew|2025-01-01|Frontiers in artificial intelligence and applications|https://github.com/hoseinkhs/AdaptiveSelectionFL|https://doi.org/10.48550/arXiv.2508.21261| 215 | |88|Private Federated Learning using Preference-Optimized Synthetic Data|Charlie Hou, Mei-Yu Wang, Yige Zhu, Daniel Lazar, Giulia Fanti|2025-01-01|arXiv|https://github.com/meiyuw/POPri.|https://openreview.net/forum?id=ZuaU2bYzlc| 216 | |89|Robust Federated Learning against Noisy Clients via Masked Optimization|Xuefeng Jiang, Tian Wen, Zhiqin Yang, Lvhua Wu, Yufeng Chen, Sheng Sun, Yuwei Wang, Min Liu|2025-01-01|arXiv|https://github.com/Sprinter1999/MaskedOptim|https://doi.org/10.48550/arXiv.2506.02079| 217 | |90|SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning|Heyi Zhang, Yule Liu, Xinlei He, Jun Wu, Tianshuo Cong, Xinyi Huang|2025-01-01|arXiv|https://github.com/vio1etus/FLPoison.|https://doi.org/10.48550/arXiv.2502.03801| 218 | |91|UFGraphFR: Graph Federation Recommendation System based on User Text description features|Xudong Wang, Qingbo Hao, Xu Cheng, Yingyuan Xiao|2025-01-01|arXiv|https://github.com/trueWangSyutung/UFGraphFR.|http://arxiv.org/abs/2501.08044v3| 219 | |92|Subgraph Federated Learning for Local Generalization|Sungwon Kim, Yoonho Lee, Yunhak Oh, Namkyeong Lee, Sukwon Yun, Junseok Lee, Sein Kim, Carl Yang, Chanyoung Park|2025-01-01|ICLR|https://github.com/sung-won-kim/FedLoG|https://openreview.net/forum?id=cH65nS5sOz| 220 | |93|The Cost of Local and Global Fairness in Federated Learning|Yuying Duan, Gelei Xu, Yiyu Shi, Michael Lemmon|2025-01-01||https://github.com/papersubmission678/The-cost-of-local-and-global-fairness-in-FL|https://proceedings.mlr.press/v258/duan25a.html| 221 | |94|Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition|Hu Ding, Yan Yan, Yang Lu, Jing-Hao Xue, Hanzi Wang|2025-01-01||https://github.com/mobei1006/AMY.|http://arxiv.org/abs/2501.01816v1| 222 | |95|UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration|Chung-ju Huang, Yuanpeng He, Xiao Han, Wenpin Jiao, Zhi Jin, Leye Wang|2025-01-01|arXiv|https://github.com/Chung-ju/Unitrans|http://arxiv.org/abs/2501.11388v1| 223 | |96|kMoL: an open-source machine and federated learning library for drug discovery|Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Ma...|2025-01-01|Journal of Cheminformatics|https://github.com/elix-tech/kmol|https://doi.org/10.1186/s13321-025-00967-9| 224 | |97|sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites|Dev Gurung, Shiva Raj Pokhrel|2025-01-01|arXiv|https://github.com/s222416822/satQFL.|https://doi.org/10.48550/arXiv.2509.16504| 225 | |98|MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning|Lishan Yang, Wei Zhang, Quan Z. Sheng, Weitong Chen, Lina Yao, Weitong Chen, Ali Shakeri|2025-01-01|OpenAlex|https://github.com/gotobcn8/MMiC.|https://doi.org/10.48550/arXiv.2505.06911| 226 | |99|On the Out-of-Distribution Backdoor Attack for Federated Learning|Jin-Sen Xu, Zikai Zhang, Rui Hu|2025-01-01|OpenAlex|https://github.com/JiiahaoXU/SoDa-BNGuard.|https://doi.org/10.48550/arXiv.2509.13219| 227 | |100|FNBench: Benchmarking Robust Federated Learning against Noisy Labels|Xuefeng Jiang, Jia Li, Nannan Wu, Zhiyuan Wu, Xujing Li, Sheng Sun, Gang Xu, Yuwei Wang, Qi Li, Min Liu|2025-01-01|OpenAlex|https://github.com/Sprinter1999/FNBench.|https://doi.org/10.36227/techrxiv.172503083.36644691/v1| 228 | |101|Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction|Chuanting Zhang, Haixia Zhang, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini|2025-01-01|IEEE Transactions on Cognitive Communications and Networking|https://github.com/chuanting/FedGCC.|https://doi.org/10.48550/arXiv.2501.00732| 229 | |102|FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching|Qifan Yan, Andrew Liu, Shiqi He, Mathias Lécuyer, Ivan Beschastnikh|2025-01-01|INFOCOM|https://github.com/DistributedML/FedFetch|https://doi.org/10.1109/INFOCOM55648.2025.11044717| 230 | |103|A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation|Yufei Ma, Hanwen Zhang, Qiya Yang, Guibo Luo, Yuesheng Zhu|2025-01-01|Frontiers in artificial intelligence and applications|https://github.com/LMIAPC/one-shot-fl-medical.|https://doi.org/10.48550/arXiv.2507.19045| 231 | |104|Allosteric Feature Collaboration for Model-Heterogeneous Federated Learning|Baoyao Yang, Pong C. Yuen, Yiqun Zhang, An Zeng|2025-01-01|IEEE Transactions on Neural Networks and Learning Systems|https://github.com/ybaoyao/AlFeCo.|https://doi.org/10.1109/TNNLS.2023.3344084| 232 | |105|Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning|Zijian Wang, Xiaofei Zhang, Xin Zhang, Yukun Liu, Qiong Zhang|2025-01-01|arXiv|https://github.com/zijianwang0510/FedDRM.git.|https://doi.org/10.48550/arXiv.2509.23049| 233 | |106|Capture Global Feature Statistics for One-Shot Federated Learning|Zhenzhen Guan, Zhou Yucan, Xiaoyan Gu|2025-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/Yuqin-G/FedCGS.|https://doi.org/10.1609/aaai.v39i16.33862| 234 | |107|Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination|Ming Yang, Dongrun Li, Xin Wang, Xiaoyang Yu, Xiaoming Wu, Shibo He|2025-01-01|Frontiers in artificial intelligence and applications|https://github.com/Dongrun-Li/FedMate.git.|https://doi.org/10.48550/arXiv.2508.17954| 235 | |108|Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning|Hongyi Pan, Gorkem Durak, Elif Keles, Deniz Seyithanoglu, Zheyuan Zhang, Alpay Medetalibeyoglu, Halil Ertugrul Aktas, An...|2025-01-01|OpenAlex|https://github.com/NUBagciLab/Cyst-X.|https://doi.org/10.21203/rs.3.rs-7236860/v1| 236 | |109|FADngs: Federated Learning for Anomaly Detection|Boyu Dong, Dong Chen, Yu Wu, Siliang Tang, Yueting Zhuang|2025-01-01|IEEE Transactions on Neural Networks and Learning Systems|https://github.com/kanade00/Federated_Anomaly_detection.|https://doi.org/10.1109/TNNLS.2024.3350660| 237 | |110|Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning With Neural Architecture Search|Ruoyou Wu, Cheng Li, Juan Zou, Xinfeng Liu, Hairong Zheng, Shanshan Wang|2025-01-01|IEEE Transactions on Medical Imaging|https://github.com/ternencewu123/GAutoMRI.|https://doi.org/10.1109/TMI.2024.3432388| 238 | |111|FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks|Binbin Ding, Penghui Yang, Sheng-Jun Huang|2025-01-01|OpenAlex|https://github.com/dingbinb/FedDLAD.|https://doi.org/10.24963/ijcai.2025/559| 239 | |112|FedBiF: Communication-Efficient Federated Learning via Bits Freezing|Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong|2025-01-01|IEEE Trans. Parallel Distributed Syst.|https://github.com/Leopold1423/fedbif-tpds25.|https://doi.org/10.48550/arXiv.2509.10161| 240 | |113|FedFitTech: A Baseline in Federated Learning for Fitness Tracking|Zeyneddin Oz, Shreyas Korde, Marius Bock, Kristof Van Laerhoven|2025-01-01|arXiv|https://github.com/adap/flower|https://doi.org/10.48550/arXiv.2506.16840| 241 | |114|FedRIR: Rethinking Information Representation in Federated Learning|Yongqiang Huang, Zerui Shao, Ziyuan Yang, Zexin Lu, Yi Zhang|2025-01-01|OpenAlex|https://github.com/Deep-Imaging-Group/FedRIR.|https://doi.org/10.48550/arXiv.2502.00859| 242 | |115|GPT-FL: Generative Pre-trained Model-Assisted Federated Learning|Tuo Zhang, Tiantian Feng, Samiul Alam, Dimitrios Dimitriadis, Sunwoo Lee, Mi Zhang, Shrikanth S. Narayanan, Salman Avest...|2025-01-01|CVPR Workshops|https://github.com/AvestimehrResearchGroup/GPT-FL.|https://openaccess.thecvf.com/content/CVPR2025W/FedVision/html/Zhang_GPT-FL_Generative_Pre-trained_Model-Assisted_Federated_Learning_CVPRW_2025_paper.html| 243 | |116|From continuous pre-training to alignment: A comprehensive toolkit for large language models in federated learning|Zhuo Zhang, Yukun Zhang, Guanzhong Chen, Lizhen Qu, Xun Zhou, Hui Wang, Zenglin Xu|2025-01-01|Neurocomputing|https://github.com/iezhuozhuo/f4llm.|https://doi.org/10.1016/j.neucom.2025.130572| 244 | |117|Fedgac: optimizing generalization in personalized federated learning via adaptive initialization and strategic client selection|Yichun Yu, Xiaoyi Yang, Zheping Chen, Yuqing Lan, Zhihuan Xing, Dan Yu|2025-01-01|Research Square (Research Square)|https://github.com/buaaYYC/FedGAC.git.|https://doi.org/10.21203/rs.3.rs-4646721/v1| 245 | |118|Federated learning for digital twin applications: a privacy-preserving and low-latency approach|Jie Li, Dong Wang|2025-01-01|PeerJ Computer Science|https://github.com/fujianU/federated-learning|https://doi.org/10.7717/peerj-cs.2877| 246 | |119|Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL|Kevin Ta, Patrick Foley, Mattson Thieme, Abhishek Pandey, Prashant Shah|2025-01-01|arXiv|https://github.com/securefederatedai/openfl|http://arxiv.org/abs/2501.12523v1| 247 | |120|FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling|Hong Huang, Hai Yang, Yuan Chen, Jiaxun Ye, Dapeng Wu|2025-01-01|arXiv|https://github.com/Little0o0/FedRTS|http://arxiv.org/abs/2501.19122v2| 248 | |121|ByzFL: Research Framework for Robust Federated Learning|Marc González, Rachid Guerraoui, Rafael Pinot, Geovani Rizk, John Stephan, François Taïani|2025-01-01|arXiv|https://github.com/LPD-EPFL/byzfl.|https://doi.org/10.48550/arXiv.2505.24802| 249 | |122|FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection|Yuqi Li, Xingyou Lin, Kai Zhang, Chuanguang Yang, Zhongliang Guo, Jianping Gou, Yanli Li|2025-01-01|arXiv|https://github.com/itsnotacie/NN-FedKD-hybrid|http://arxiv.org/abs/2501.04066v1| 250 | |123|FedKDC: Consensus-Driven Knowledge Distillation for Personalized Federated Learning in EEG-Based Emotion Recognition|Xihang Qiu, Wanyong Qiu, Ye Zhang, Kun Qian, Chun Guang Li, Bin Hu, Björn W. Schuller, Yoshiharu Yamamoto|2025-01-01|IEEE Journal of Biomedical and Health Informatics|https://github.com/wdqdp/FedKDC.|https://doi.org/10.1109/jbhi.2025.3562090| 251 | |124|Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning|Sijia Chen, Ningxin Su, Bao-Chun Li|2024-12-27|OpenAlex|https://github.com/TL-System/plato|https://doi.org/10.1109/ICDCS60910.2024.00087| 252 | |125|SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning|Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi, Ivan V. Bajić|2024-12-18|arXiv|https://github.com/ChamaniS/SplitFedZip|https://doi.org/10.48550/arXiv.2412.17150| 253 | |126|Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression|Junliang Lyu, Yixuan Zhang, Xiaoling Lu, Feng Zhou|2024-12-14|OpenAlex|https://github.com/JunliangLv/task_diversity_BFL.|https://doi.org/10.48550/arXiv.2412.10897| 254 | |127|Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation|SeungBum Ha, Taehwan Lee, Jiyoun Lim, Sung Whan Yoon|2024-12-11|Pattern Recognition Letters|https://github.com/Seung-B/FL-PSG.|https://doi.org/10.1016/j.patrec.2025.07.020| 255 | |128|One-shot Federated Learning via Synthetic Distiller-Distillate Communication|Junyuan Zhang, Songhua Liu, Xinchao Wang|2024-12-06|NeurIPS|https://github.com/Carkham/FedSD2C|http://papers.nips.cc/paper_files/paper/2024/hash/ba0ad9d1e0c737800b2340b9cd68c208-Abstract-Conference.html| 256 | |129|FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning|Jiayu Liu, Yong Wang, Nianbin Wang, Jing Yang, Xiaohui Tao|2024-12-05|arXiv|https://github.com/liuvvvvv1/FedDW.|https://doi.org/10.48550/arXiv.2412.04521| 257 | |130|FedAH: Aggregated Head for Personalized Federated Learning|Pengzhan Zhou, Yuepeng He, Yijun Zhai, Kaixin Gao, Chao Chen, Zhida Qin, Chong Zhang, Songtao Guo|2024-12-02|OpenAlex|https://github.com/heyuepeng/FedAH.|https://doi.org/10.1109/swc62898.2024.00068| 258 | |131|Vertical Federated Unlearning via Backdoor Certification|Mengde Han, Tianqing Zhu, Lefeng Zhang, Huan Huo, Wanlei Zhou|2024-12-01|arXiv|https://github.com/mengde-han/VFL-unlearn.|http://arxiv.org/abs/2412.11476v1| 259 | |132|Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning|Weihang Chen, Cheng‐Han Yang, Jie Ren, Zhiqiang Li, Zheng Wang|2024-12-01|OpenAlex|https://github.com/lancasterJie/FLAYER|https://doi.org/10.48550/arXiv.2412.07062| 260 | |133|BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT|Zhengyu Ju, Tongquan Wei, Fuke Shen|2024-12-01|arXiv|https://github.com/juzehao/BEFL|https://doi.org/10.48550/arXiv.2412.03950| 261 | |134|Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models|Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer|2024-12-01|arXiv|https://github.com/dipamgoswami/FedCOF.|https://doi.org/10.48550/arXiv.2412.14326| 262 | |135|Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning|Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer|2024-12-01|arXiv|https://github.com/dipamgoswami/FedCOF.|http://arxiv.org/abs/2412.14326v3| 263 | |136|DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices|Yongzhe Jia, Xuyun Zhang, Hongsheng Hu, Kim-Kwang Raymond Choo, Lianyong Qi, Xiaolong Xu, Amin Beheshti, Wanchun Dou|2024-12-01|NeurIPS|https://github.com/jyzgh/DapperFL.|http://papers.nips.cc/paper_files/paper/2024/hash/17a1a1439421f1837e10cd612bf92861-Abstract-Conference.html| 264 | |137|FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction|Yuepeng He, Pengzhan Zhou, Yijun Zhai, Fang Qu, Zhida Qin, Mingyan Li, Songtao Guo|2024-12-01|IEEE Transactions on Cloud Computing|https://github.com/heyuepeng/PFLlibVSP|https://doi.org/10.48550/arXiv.2412.01281| 265 | |138|Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL|Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč|2024-12-01|arXiv|https://github.com/Optimization-and-Machine-Learning-Lab/energy_fed_trpo.git|http://arxiv.org/abs/2412.20946v2| 266 | |139|FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning|Minjun Kim, Min‐Jee Kim, Jinhoon Jeong|2024-12-01|arXiv|https://github.com/danny0628/FedCAR.|https://doi.org/10.48550/arXiv.2412.11463| 267 | |140|FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients|Han Liang, Ziwei Zhan, Weijie Liu, Xiaoxi Zhang, Chee Wei Tan, Xu Chen|2024-11-04|Frontiers in artificial intelligence and applications|https://github.com/liangh68/FedReMa.|https://doi.org/10.48550/arXiv.2411.01825| 268 | |141|Identify Backdoored Model in Federated Learning via Individual Unlearning|Jiahao Xu, Zikai Zhang, Rui Hu|2024-11-01|2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|https://github.com/JiiahaoXU/MASA|https://doi.org/10.1109/WACV61041.2025.00773| 269 | |142|Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures|Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng|2024-11-01|arXiv|https://github.com/zyc140345/FedAMoLE|http://arxiv.org/abs/2411.19128v3| 270 | |143|FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning|Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub, Karthik Nandakumar|2024-11-01|CVPR Workshops|https://github.com/JosephGeoBenjamin/FedSECA-ByzantineTolerance|https://openaccess.thecvf.com/content/CVPR2025W/FedVision/html/Benjamin_FedSECA_Sign_Election_and_Coordinate-wise_Aggregation_of_Gradients_for_Byzantine_CVPRW_2025_paper.html| 271 | |144|FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles|Yijun Zhai, Pengzhan Zhou, Yuepeng He, Fang Qu, Zhida Qin, Xianlong Jiao, Guiyan Liu, Songtao Guo|2024-11-01|arXiv|https://github.com/yjzhai-cs/FedRAV.|http://arxiv.org/abs/2411.13979v1| 272 | |145|FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework|Yuchen He, Chuyun Shen, Xiangfeng Wang, Bo Jin|2024-11-01|arXiv|https://github.com/ycheoo/FPPL.|http://arxiv.org/abs/2411.01904v3| 273 | |146|Energy-efficient Federated Learning with Dynamic Model Size Allocation|M. S. Chaitanya Kumar, Sai Satya Narayana J, Yunkai Bao, Xin Wang, Steve Drew|2024-11-01|2021 IEEE International Conference on Big Data (Big Data)|https://github.com/denoslab/CAMA.|https://doi.org/10.1109/BigData62323.2024.10825664| 274 | |147|Adaptive Client Selection with Personalization for Communication Efficient Federated Learning|Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa, Luiz F. Bittencourt, Eduardo Cerqueira, Antonio A. F. Lourei...|2024-11-01|Ad Hoc Networks|https://github.com/AllanMSouza/ACSP-FL|https://doi.org/10.1016/j.adhoc.2024.103462| 275 | |148|Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning|Minghui Chen, Meirui Jiang, Xin Zhang, Qi Dou, Zehua Wang, Xiaoxiao Li|2024-10-31|NeurIPS|https://github.com/ubc-tea/Local-Superior-Soups|http://papers.nips.cc/paper_files/paper/2024/hash/24f7b98aef14fcd68acf3c941af1b59e-Abstract-Conference.html| 276 | |149|Vertical Federated Learning with Missing Features During Training and Inference|Pedro Valdeira, Shiqiang Wang, Yuejie Chi|2024-10-29|arXiv|https://github.com/Valdeira/LASER-VFL.|https://openreview.net/forum?id=OXi1FmHGzz| 277 | |150|FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data|Yukun Zhang, Guanzhong Chen, Zenglin Xu, Jianyong Wang, Dun Zeng, Junfan Li, Jinghua Wang, Yuan Qi, Irwin King|2024-10-27|arXiv|https://github.com/SMILELab-FL/FedCVD.|https://doi.org/10.48550/arXiv.2411.07050| 278 | |151|FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator|Sunny Gupta, Nikita Jangid, Amit Sethi|2024-10-04|arXiv|https://github.com/sunnyinAI/FedStein|https://doi.org/10.48550/arXiv.2410.03499| 279 | |152|Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift|Junbao Chen, Jingfeng Xue, Yong Wang, Zhenyan Liu, Xuhui Huang|2024-10-01|NeurIPS|https://github.com/Chen-Junbao/FedCCFA.|http://papers.nips.cc/paper_files/paper/2024/hash/942e820be4aa112509b3a281ff398851-Abstract-Conference.html| 280 | |153|Federated Black-Box Adaptation for Semantic Segmentation|Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel|2024-10-01|arXiv|https://github.com/JayParanjape/blackfed|http://arxiv.org/abs/2410.24181v1| 281 | |154|A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning|Jun Bai, Yiliao Song, Di Wu, Atul Sajjanhar, Yong Xiang, Wei Zhou, Xiaohui Tao, Yan Li, Yue Li|2024-10-01|OpenAlex|https://github.com/Jun-B0518/FedHydra.|https://doi.org/10.48550/arXiv.2410.21119| 282 | |155|Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning|Hassan Ali, Surya Nepal, Salil S. Kanhere, Sanjay Jha|2024-10-01|arXiv|https://github.com/hassanalikhatim/AGSD.|http://arxiv.org/abs/2410.11205v1| 283 | |156|An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration|Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni|2024-10-01|Trans. Mach. Learn. Res.|https://github.com/siomvas/ANFR.|https://openreview.net/forum?id=GtdYFLsblb| 284 | |157|Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning|Zhilong Li, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew-Soon Ong, Mengmeng Chen, Qiqi Liu, Qicheng Lao, Xiaoxiao Li, Yu Han|2024-10-01|Lecture notes in computer science|https://github.com/Xiaoni-61/DH-Benchmark.|https://doi.org/10.1007/978-3-031-82240-7_6| 285 | |158|DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation|Meilu Zhu, Axiu Mao, Jun Liu, Yixuan Yuan|2024-10-01|arXiv|https://github.com/CUHK-AIM-Group/DEeR.|http://arxiv.org/abs/2410.12926v1| 286 | |159|PARDON: Privacy-Aware and Robust Federated Domain Generalization|Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach|2024-10-01|arXiv|https://github.com/judydnguyen/PARDON-FedDG.|http://arxiv.org/abs/2410.22622v2| 287 | |160|Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views|Xinyue Chen, Yazhou Ren, Jie Xu, Fangfei Lin, Xiaorong Pu, Yang Yang|2024-10-01|arXiv|https://github.com/5Martina5/FMCSC|http://arxiv.org/abs/2410.09484v1| 288 | |161|Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models|Jun Luo, Chen Chen, Shandong Wu|2024-10-01|arXiv|https://github.com/ljaiverson/pFedMoAP.|http://arxiv.org/abs/2410.10114v4| 289 | |162|FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference|Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye|2024-10-01|arXiv|https://github.com/OakleyTan/FedSSP.|http://arxiv.org/abs/2410.20105v1| 290 | |163|FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models|Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma|2024-10-01|arXiv|https://github.com/RaghavSinghal10/fedex-lora.|http://arxiv.org/abs/2410.09432v4| 291 | |164|FedGraph: A Research Library and Benchmark for Federated Graph Learning|Yuhang Yao, Yuan Li, Xinyi Fan, Junhao Li, Kay Liu, Weizhao Jin, Yu Yang, Srivatsan Ravi, Philip S. Yu, Carlee Joe-Wong|2024-10-01|arXiv|https://github.com/FedGraph/fedgraph|http://arxiv.org/abs/2410.06340v3| 292 | |165|FedGMark: Certifiably Robust Watermarking for Federated Graph Learning|Yuxin Yang, Qiang Li, Yuan Hong, Binghui Wang|2024-10-01|arXiv|https://github.com/Yuxin104/FedGMark.|http://arxiv.org/abs/2410.17533v1| 293 | |166|Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation|Matthis Manthe, Carole Lartizien, Stefan Duffner|2024-10-01||https://github.com/MatthisManthe/DDI_SCFL|https://doi.org/10.1007/978-3-031-70359-1_22| 294 | |167|Evaluating Federated Kolmogorov-Arnold Networks on Non-IID Data|Arthur Mendonça Sasse, Claudio Miceli de Farias|2024-10-01|arXiv|https://github.com/artsasse/fedkan|http://arxiv.org/abs/2410.08961v1| 295 | |168|FACMIC: Federated Adaptative CLIP Model for Medical Image Classification|Yihang Wu, Christian Desrosiers, Ahmad Chaddad|2024-10-01|arXiv|https://github.com/AIPMLab/FACMIC.|http://arxiv.org/abs/2410.14707v1| 296 | |169|FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection|Xinting Liao, Weiming Liu, Pengyang Zhou, Fengyuan Yu, Jiahe Xu, Jun Wang, Wenjie Wang, Chaochao Chen, Xiaolin Zheng|2024-10-01|arXiv|https://github.com/XeniaLLL/FOOGD-main.git.|http://arxiv.org/abs/2410.11397v2| 297 | |170|FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning|Xinpeng Wang, Yongxin Guo, Xiaoying Tang|2024-10-01|arXiv|https://github.com/sanphouwang/fedccrl|http://arxiv.org/abs/2410.11267v4| 298 | |171|FedCert: Federated Accuracy Certification|Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le ...|2024-10-01|arXiv|https://github.com/thanhhff/FedCert|http://arxiv.org/abs/2410.03067v1| 299 | |172|FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization|Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh|2024-10-01|OpenAlex|https://github.com/nmduonggg/PEPSY|https://doi.org/10.1109/NCA61908.2024.00048| 300 | |173|Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework|Zilinghan Li, Shilan He, Ze Yang, Minseok Ryu, Kibaek Kim, Ravi K. Madduri|2024-09-17|OpenAlex|https://github.com/APPFL/APPFL.|https://doi.org/10.1109/ccgrid64434.2025.00031| 301 | |174|FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation|Anupam Borthakur, Asim Kumar Manna, Aditya Kasliwal, Dipayan Dewan, Debdoot Sheet|2024-09-17|OpenAlex|https://github.com/anupamkliv/FedERA.|https://doi.org/10.1109/flta63145.2024.10840072| 302 | |175|Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation|Jiahao Xu, Zikai Zhang, Rui Hu|2024-09-02|2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|https://github.com/JiiahaoXU/LASA|https://doi.org/10.1109/WACV61041.2025.00154| 303 | |176|Buffer-based Gradient Projection for Continual Federated Learning|Shenghong Dai, Jy-yong Sohn, Yicong Chen, S M Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, K...|2024-09-02|Trans. Mach. Learn. Res.|https://github.com/shenghongdai/Fed-A-GEM.|https://openreview.net/forum?id=Xz5IcOizQ6| 304 | |177|Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration|Mahdi Morafah, Vyacheslav Kungurtsev, Hsiao-Yun Chang, Chen Chen, Bill Yuchen Lin|2024-09-01||https://github.com/MMorafah/TAKFL|http://papers.nips.cc/paper_files/paper/2024/hash/e6d1d6195f6f3e32a930643e0ef46332-Abstract-Conference.html| 305 | |178|FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation|Li Wang, Qiang Wu, Min Xu|2024-09-01|arXiv|https://github.com/Lili1013/FedPCL|http://arxiv.org/abs/2409.03294v2| 306 | |179|FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning|Xiuhua Lu, Peng Li, Xuefeng Jiang|2024-09-01|arXiv|https://github.com/18sym/FedLF.|http://arxiv.org/abs/2409.12105v1| 307 | |180|Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation|Ruojun Zhou, Lisha Qu, Lei Zhang, Ziming Li, Hongwei Yu, Bing Luo|2024-09-01|arXiv|https://github.com/Arnold-Jun/Fed-MUnet.|http://arxiv.org/abs/2409.01020v1| 308 | |181|FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations|Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, Ang Li|2024-09-01|OpenReview|https://github.com/ATP-1010/FederatedLLM.|https://openreview.net/pdf/fe980ffa952becc26f4181f1ba47b1a2a35fde0d.pdf| 309 | |182|Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning|Jinglin Liang, Jin Zhong, Hanlin Gu, Zhongqi Lu, Xingxing Tang, Gang Dai, Shuangping Huang, Lixin Fan, Qiang Yang|2024-09-01|arXiv|https://github.com/jinglin-liang/DDDR.|http://arxiv.org/abs/2409.01128v2| 310 | |183|FedSlate:A Federated Deep Reinforcement Learning Recommender System|Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Yaochu Jin|2024-09-01|arXiv|https://github.com/TianYaDY/FedSlate|http://arxiv.org/abs/2409.14872v2| 311 | |184|Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics|Jiaxiang Geng, Beilong Tang, Boyan Zhang, Jiaqi Shao, Bing Luo|2024-08-30|OpenAlex|https://github.com/FedCampus/FedCampus_Flutter.|https://doi.org/10.48550/arXiv.2409.00327| 312 | |185|VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification|Yungi Cho, Woorim Han, Miseon Yu, Younghan Lee, Ho Bae, Yunheung Paek|2024-08-28|Lecture notes in computer science|https://github.com/blingcho/VFLIP-esorics24|https://doi.org/10.1007/978-3-031-70903-6_15| 313 | |186|Understanding Byzantine Robustness in Federated Learning with A Black-box Server|Fangyuan Zhao, Yuexiang Xie, Xuebin Ren, Bolin Ding, Shusen Yang, Yaliang Li|2024-08-12|arXiv|https://github.com/alibaba/FederatedScope|https://doi.org/10.48550/arXiv.2408.06042| 314 | |187|Tackling Noisy Clients in Federated Learning with End-to-end Label Correction|Xuefeng Jiang, Sheng Sun, Jia Li, Jingjing Xue, Runhan Li, Zhiyuan Wu, Gang Xu, Yuwei Wang, Min Liu|2024-08-01|OpenAlex|https://github.com/Sprinter1999/FedELC.|https://doi.org/10.48550/arXiv.2408.04301| 315 | |188|UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks|Atefe Hassani, Islem Rekik|2024-08-01|arXiv|https://github.com/basiralab/UniFed.|http://arxiv.org/abs/2408.07075v2| 316 | |189|Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability|Mario Padilla Rodriguez, Mohamed Nafea|2024-08-01|arXiv|https://github.com/padillma1/Heart-Disease-Classification-on-UCI-dataset-and-Shapley-Interpretability-Analysis.|http://arxiv.org/abs/2408.06183v2| 317 | |190|DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing|Xueying Gu, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief|2024-08-01|arXiv|https://github.com/qiongwu86/Federated-SSL-task-offloading-and-resource-allocation|http://arxiv.org/abs/2408.14831v2| 318 | |191|DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV|Xueying Gu, Qiong Wu, Pingyi Fan, Qiang Fan, Nan Cheng, Wen Chen, Khaled B. Letaief|2024-08-01|arXiv|https://github.com/qiongwu86/DRL-BFSSL|http://arxiv.org/abs/2408.09194v2| 319 | |192|FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation|Philip Schutte, Valentina Corbetta, Regina Beets-Tan, Wilson Silva|2024-08-01|arXiv|https://github.com/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement|http://arxiv.org/abs/2408.11701v1| 320 | |193|Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation|Li Wang, Shoujin Wang, Quangui Zhang, Qiang Wu, Min Xu|2024-08-01|arXiv|https://github.com/Lili1013/FUPM.|http://arxiv.org/abs/2408.14689v1| 321 | |194|Mobility-Aware Federated Self-supervised Learning in Vehicular Network|Xueying Gu, Qiong Wu, Pingyi Fan, Qiang Fan|2024-08-01|arXiv|https://github.com/qiongwu86/FLSimCo|http://arxiv.org/abs/2408.00256v2| 322 | |195|Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery|Jialang Xu, Jiacheng Wang, Lequan Yu, Danail Stoyanov, Yueming Jin, Evangelos B. Mazomenos|2024-08-01|arXiv|https://github.com/wzjialang/PFedSIS.|http://arxiv.org/abs/2408.03208v2| 323 | |196|Federated Graph Learning with Structure Proxy Alignment|Xingbo Fu, Zihan Chen, Binchi Zhang, Chen Chen, Jundong Li|2024-08-01|arXiv|https://github.com/xbfu/FedSpray.|http://arxiv.org/abs/2408.09393v1| 324 | |197|FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights|Gaoxuan Li, Chern Hong Lim, Qiyao Ma, Xinyu Tang, Hwa Hui Tew, Fan Ding, Xuewen Luo|2024-07-30|2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)|https://github.com/Glen909/FedBChain|https://doi.org/10.1109/smc54092.2024.10831884| 325 | |198|FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios|Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen|2024-07-17|IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems|https://github.com/mastlab-T3S/FlexFL|https://doi.org/10.1109/tcad.2024.3444695| 326 | |199|FedCEA: Efficient Adaptive Personalized Federated Learning based on Critical Learning Periods|Yichun Yu, Xiaoyi Yang, Zheping Chen, Yuqing Lan, Zhihuan Xing, Dan Yu|2024-07-16|Research Square (Research Square)|https://github.com/buaaYYC/FedCEA|https://doi.org/10.21203/rs.3.rs-4630899/v1| 327 | |200|Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing|Cui Zhang, Wenjun Zhang, Qiong Wu, Pingyi Fan, Qiang Fan, Jiangzhou Wang, Khaled B. Letaief|2024-07-11|IEEE Internet of Things Journal|https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient|https://doi.org/10.48550/arXiv.2407.08462| 328 | |201|FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption|Y. H. Pan, Chao Zheng, Wang He, Jing Yang, Hongjia Li, Wang Liming|2024-07-04|Cybersecurity|https://github.com/yooopan/FedSHE|https://doi.org/10.1186/s42400-024-00232-w| 329 | |202|Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging|Zhipeng Deng, Luyang Luo, Hao Chen|2024-07-01|arXiv|https://github.com/dzp2095/FCU.|http://arxiv.org/abs/2407.02356v1| 330 | |203|Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning|Son Nguyen, Thinh Viet Nguyen, Khoa D. Doan, Kok‐Seng Wong|2024-07-01|arXiv|https://github.com/nguyenhongson1902/Venomancer.|https://doi.org/10.48550/arXiv.2407.03144| 331 | |204|CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities|Pranav Poudel, Prashant Shrestha, Sanskar Amgain, Yash Raj Shrestha, Prashnna K. Gyawali, Binod Bhattarai|2024-07-01|Lecture notes in computer science|https://github.com/bhattarailab/CAR-MFL|https://doi.org/10.1007/978-3-031-72117-5_10| 332 | |205|Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses|Yuxin Yang, Qiang Li, Jinyuan Jia, Yuan Hong, Binghui Wang|2024-07-01|arXiv|https://github.com/Yuxin104/Opt-GDBA.|http://arxiv.org/abs/2407.08935v1| 333 | |206|F-KANs: Federated Kolmogorov-Arnold Networks|Engin Zeydan, Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Marius Caus, Abdullah Aydeger|2024-07-01|arXiv|https://github.com/ezeydan/F-KANs.git|http://arxiv.org/abs/2407.20100v3| 334 | |207|PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning|Muhammad Anwar Ma'sum, Mahardhika Pratama, Savitha Ramasamy, Lin Liu, Habibullah Habibullah, Ryszard Kowalczyk|2024-07-01|arXiv|https://github.com/anwarmaxsum/PIP.|http://arxiv.org/abs/2407.20705v1| 335 | |208|Personalized Federated Continual Learning via Multi-granularity Prompt|Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li|2024-07-01|arXiv|https://github.com/SkyOfBeginning/FedMGP.|http://arxiv.org/abs/2407.00113v1| 336 | |209|Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports|Malte Tölle, Lukas Burger, Halvar Kelm, Florian André, Peter Bannas, Gerhard Diller, Norbert Frey, Philipp Garthe, Stefa...|2024-07-01|International Journal of Computer Assisted Radiology and Surgery|https://github.com/Cardio-AI/fl-multi-modal-dataset-creation|https://doi.org/10.1007/s11548-025-03327-y| 337 | |210|DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations|Guogang Zhu, Xuefeng Liu, Jianwei Niu, Shaojie Tang, Xinghao Wu, Jiayuan Zhang|2024-07-01|ACM Multimedia|https://github.com/GuogangZhu/DualFed.|https://doi.org/10.48550/arXiv.2407.17754| 338 | |211|FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels|Malte Tölle, Fernando Navarro, Sebastian Eble, Ivo Wolf, Bjoern Menze, Sandy Engelhardt|2024-07-01|arXiv|https://github.com/Cardio-AI/FUNAvg.|http://arxiv.org/abs/2407.07488v1| 339 | |212|FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness|Yangyang Xiang, Nannan Wu, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan|2024-07-01|arXiv|https://github.com/HUSTxyy/FedIA.|http://arxiv.org/abs/2407.02280v2| 340 | |213|FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging|Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha, Samrat Mondal|2024-07-01|arXiv|https://github.com/Pranabiitp/FedMRL|http://arxiv.org/abs/2407.05800v1| 341 | |214|FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging|Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra, Thomas Hartvigsen, Philip Torr, Bernard Ghanem, Adel Bibi, Marzyeh Ghas...|2024-07-01|arXiv|https://github.com/m1k2zoo/FedMedICL|http://arxiv.org/abs/2407.08822v1| 342 | |215|Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation|Marawan Elbatel, Hualiang Wang, Jixiang Chen, Hao Wang, Xiaomeng Li|2024-07-01|arXiv|https://github.com/xmed-lab/SemiAnAgg.|http://arxiv.org/abs/2407.10327v2| 343 | |216|Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning|Wenhua Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief|2024-07-01|arXiv|https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning|http://arxiv.org/abs/2407.02342v1| 344 | |217|A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning|Anqi Zhou, Yezheng Liu, Yidong Chai, Hongyi Zhu, Xinyue Ge, Yuanchun Jiang, Meng Wang|2024-06-30|arXiv|https://github.com/brick-brick/WPCRAM.|https://doi.org/10.48550/arXiv.2407.00719| 345 | |218|Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition|Xinghao Wu, Xuefeng Liu, Jianwei Niu, Haolin Wang, Shaojie Tang, Guogang Zhu, Hao Su|2024-06-28|ACM Multimedia|https://github.com/XinghaoWu/FedDecomp.|https://doi.org/10.48550/arXiv.2406.19931| 346 | |219|Communication-efficient Vertical Federated Learning via Compressed Error Feedback|Pedro Valdeira, João Xavier, Cláudia Soares, Yuejie Chi|2024-06-20|IEEE Transactions on Signal Processing|https://github.com/Valdeira/EF-VFL.|https://doi.org/10.23919/eusipco63174.2024.10715377| 347 | |220|Synergizing Foundation Models and Federated Learning: A Survey|Shenghui Li, Fanghua Ye, Meng Fang, Jiaxu Zhao, Yun-Hin Chan, Edith C. -H. Ngai, Thiemo Voigt|2024-06-18|arXiv|https://github.com/lishenghui/awesome-fm-fl.|https://doi.org/10.48550/arXiv.2406.12844| 348 | |221|Low-Resource Machine Translation through the Lens of Personalized Federated Learning|Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina|2024-06-18|OpenAlex|https://github.com/VityaVitalich/MeritOpt.|https://doi.org/10.18653/v1/2024.findings-emnlp.514| 349 | |222|Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities|F. Wagner, Wentian Xu, Pramit Saha, Ziyun Liang, Daniel Whitehouse, David Menon, Natalie L. Voets, J. Alison Noble, Kons...|2024-06-17|2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|https://github.com/FelixWag/FL-MultiDisease-MRI|https://doi.org/10.1109/wacv61041.2025.00045| 350 | |223|FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models|Rui Ye, Rui Ge, Xinyu Zhu, Jingyi Chai, Yaxin Du, Yang Liu, Yanfeng Wang, Siheng Chen|2024-06-07|NeurIPS|https://github.com/rui-ye/FedLLM-Bench.|http://papers.nips.cc/paper_files/paper/2024/hash/c8cdab0e890c59255c27977072fdb0f0-Abstract-Datasets_and_Benchmarks_Track.html| 351 | |224|FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles|Cyprien Quéméneur, Soumaya Cherkaoui|2024-06-05|arXiv|https://github.com/cyprienquemeneur/fedpylot.|https://doi.org/10.48550/arXiv.2406.03611| 352 | |225|A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder|Jingjing Zheng, Xin Yuan, Kai Li, Wei Ni, Eduardo Tovar, Jon Crowcroft|2024-06-02|arXiv|https://github.com/jjzgeeks/LayerCAM-AE|https://doi.org/10.48550/arXiv.2406.02605| 353 | |226|SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead|Minsu Kim, Walid Saad, Merouane Debbah, Choong Seon Hong|2024-06-01|NeurIPS|https://github.com/news-vt/SpaFL_NeruIPS_2024|http://papers.nips.cc/paper_files/paper/2024/hash/9d6d351ba8028a50382f42a065d31bf0-Abstract-Conference.html| 354 | |227|FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation|Tong Xia, Abhirup Ghosh, Xinchi Qiu, Cecilia Mascolo|2024-06-01|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/XTxiatong/FLea.git.|https://doi.org/10.48550/arXiv.2406.09547| 355 | |228|FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity|Zhaobin Sun, Nannan Wu, Junjie Shi, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan|2024-06-01|arXiv|https://github.com/szbonaldo/FedMLP.|http://arxiv.org/abs/2406.18995v1| 356 | |229|Federated Face Forgery Detection Learning with Personalized Representation|Decheng Liu, Zhan Dang, Chunlei Peng, Nannan Wang, Ruimin Hu, Xinbo Gao|2024-06-01|arXiv|https://github.com/GANG370/PFR-Forgery.|http://arxiv.org/abs/2406.11145v1| 357 | |230|Redefining Contributions: Shapley-Driven Federated Learning|Nurbek Tastan, Samar Fares, Toluwani Aremu, Samuel Horvath, Karthik Nandakumar|2024-06-01|OpenAlex|https://github.com/tnurbek/shapfed.|https://www.ijcai.org/proceedings/2024/554| 358 | |231|Pursuing Overall Welfare in Federated Learning through Sequential Decision Making|Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee|2024-05-31|ICML|https://github.com/vaseline555/AAggFF|https://openreview.net/forum?id=foPMkomvk1| 359 | |232|Share Your Secrets for Privacy! Confidential Forecasting with Vertical Federated Learning|Aditya Shankar, Lydia Y. Chen, Jérémie Decouchant, Dimitra Gkorou, Rihan Hai|2024-05-31|arXiv|https://github.com/adis98/STV|https://doi.org/10.48550/arXiv.2405.20761| 360 | |233|Federated Learning with Bilateral Curation for Partially Class-Disjoint Data|Ziqing Fan, Ruipeng Zhang, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang|2024-05-29|NeurIPS|https://github.com/MediaBrain-SJTU/FedGELA.git.|http://papers.nips.cc/paper_files/paper/2023/hash/65b721a1df04c1098567f70d483d6468-Abstract-Conference.html| 361 | |234|Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping|Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu, Ya Zhang, Yanfeng Wang|2024-05-29|Trans. Mach. Learn. Res.|https://github.com/MediaBrain-SJTU/FedMR.git.|https://openreview.net/forum?id=jLJTqJXAG7| 362 | |235|FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning|Zihui Wang, Zheng Wang, Lingjuan Lyu, Zhaopeng Peng, Zhicheng Yang, Chenglu Wen, Rongshan Yu, Cheng Wang, Xiaoliang Fan|2024-05-28|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/wangzihuixmu/FedSAC.|https://doi.org/10.48550/arXiv.2405.18291| 363 | |236|Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing|Yongxin Guo, Lin Wang, Xiaoying Tang, Tao Lin|2024-05-25|arXiv|https://github.com/LINs-lab/client2vec|https://doi.org/10.48550/arXiv.2405.16233| 364 | |237|A GAN-Based Data Poisoning Attack Against Federated Learning Systems and Its Countermeasure|Wei Sun, Bo Gao, Ke Xiong, Yuwei Wang|2024-05-19|arXiv|https://github.com/SSssWEIssSS/VagueGAN-Data-Poisoning-Attack-and-Its-Countermeasure|https://doi.org/10.48550/arXiv.2405.11440| 365 | |238|Guard-FL: An UMAP-Assisted Robust Aggregation for Federated Learning|Anxiao Song, Haoshuo Li, Ke Cheng, Tao Zhang, Aijing Sun, Yulong Shen|2024-05-10|IEEE Internet of Things Journal|https://github.com/XidianNSS/Guard-FL.git|https://doi.org/10.1109/jiot.2024.3399259| 366 | |239|A Survey on Contribution Evaluation in Vertical Federated Learning|Yue Cui, Chung-ju Huang, Yuzhu Zhang, Leye Wang, Lixin Fan, Xiaofang Zhou, Qiang Yang|2024-05-03|arXiv|https://github.com/cuiyuebing/VFL_CE|https://doi.org/10.48550/arXiv.2405.02364| 367 | |240|Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities|Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Sung-Ju Lee|2024-05-01|arXiv|https://github.com/AdibaOrz/FLISM.|http://arxiv.org/abs/2405.11828v2| 368 | |241|Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning|Guogang Zhu, Xuefeng Liu, Xinghao Wu, Shaojie Tang, Chao Tang, Jianwei Niu, Hao Su|2024-05-01|arXiv|https://github.com/GuogangZhu/FedDB.|http://arxiv.org/abs/2405.19789v1| 369 | |242|EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection|Yuwen Qian, Shuchi Wu, Kang Wei, Ming Ding, Di Xiao, Tao Xiang, Chuan Ma, Song Guo|2024-05-01|arXiv|https://github.com/ShuchiWu/EmInspector.|http://arxiv.org/abs/2405.13080v1| 370 | |243|Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization|Zhe Li, Bicheng Ying, Zidong Liu, Chaosheng Dong, Haibo Yang|2024-05-01|ICLR|https://github.com/ZidongLiu/DeComFL.|https://openreview.net/forum?id=omrLHFzC37| 371 | |244|AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models|Run He, Kai Tong, Di Fang, Handong Sun, Ziqian Zeng, Haoran Li, Tianyi Chen, Huiping Zhuang|2024-05-01|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|https://github.com/ZHUANGHP/Analytic-federated-learning.|https://openaccess.thecvf.com/content/CVPR2025/html/He_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html| 372 | |245|Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learning|Li Qiao, Zhen Gao, Mahdi Boloursaz Mashhadi, Deniz Gündüz|2024-05-01|arXiv|https://github.com/liqiao19/MD-AirComp.|http://arxiv.org/abs/2405.15969v2| 373 | |246|Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning|Aditya Shankar, Jérémie Decouchant, Dimitra Gkorou, Rihan Hai, Lydia Y. Chen|2024-05-01|Lecture notes in computer science|https://github.com/adis98/STV.|https://doi.org/10.1007/978-3-032-00624-0_18| 374 | |247|Unlearning during Learning: An Efficient Federated Machine Unlearning Method|Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, Qiang Yang|2024-05-01|arXiv|https://github.com/Liar-Mask/FedAU.|http://arxiv.org/abs/2405.15474v2| 375 | |248|Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning|Xueqing Zhang, Junkai Zhang, Ka-Ho Chow, Juntao Chen, Ying Mao, Mohamed Rahouti, Xiang Li, Yuchen Liu, Wenqi Wei|2024-05-01|arXiv|https://github.com/CathyXueqingZhang/DataPoisoningVis.|https://doi.org/10.48550/arXiv.2405.16707| 376 | |249|Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey|Mang Ye, Wei Shen, Bo Du, Eduard Snezhko, Vassili Kovalev, Pong C. Yuen|2024-05-01|ACM Computing Surveys|https://github.com/shentt67/VFL_Survey.|https://doi.org/10.48550/arXiv.2405.17495| 377 | |250|FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning|Mengfan Xu, Yaguang Lin|2024-04-29|IEEJ Transactions on Electrical and Electronic Engineering|https://github.com/Xumeili/FedSteg|https://doi.org/10.1002/tee.24085| 378 | |251|From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching|Nannan Wu, Zhuo Kuang, Zengqiang Yan, Li Yu|2024-04-27|OpenAlex|https://github.com/wnn2000/FFL4MIA.|https://www.ijcai.org/proceedings/2024/575| 379 | |252|Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data|Vikhyat Agrawal, Sunil V. Kalmady, Venkataseetharam Manoj Malipeddi, Manisimha Varma Manthena, Weijie Sun, Md Saiful Isl...|2024-04-26|OpenAlex|https://github.com/vikhyatt/Hospital-FL-DP.|https://doi.org/10.48550/arXiv.2405.00725| 380 | |253|Decentralized Personalized Federated Learning Based on a Conditional "Sparse-to-Sparser" Scheme|Qianyu Long, Qianxing Wang, Christos Anagnostopoulos, Daning Bi|2024-04-24|IEEE Transactions on Neural Networks and Learning Systems|https://github.com/EricLoong/da-dpfl|https://doi.org/10.1109/TNNLS.2025.3580277| 381 | |254|Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing|Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan, Pingyi Fan, Huiling Zhu, Jiangzhou Wang|2024-04-12|China Communications|https://github.com/giongwu86/By-AFLDDPG|https://doi.org/10.23919/jcc.fa.2023-0718.202408| 382 | |255|Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning|Tao Liu, Yuhang Zhang, Zhu Feng, Zhiqin Yang, Chen Xu, Dapeng Man, Wu Yang|2024-04-01||https://github.com/PhD-TaoLiu/FCBA.|https://doi.org/10.1609/aaai.v38i19.30131| 383 | |256|URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning|Duanyi Yao, Songze Li, Xueluan Gong, Sizai Hou, Gaoning Pan|2024-04-01|OpenAlex|https://github.com/duanyiyao/URVFL.|https://www.ndss-symposium.org/ndss-paper/urvfl-undetectable-data-reconstruction-attack-on-vertical-federated-learning/| 384 | |257|pfl-research: simulation framework for accelerating research in Private Federated Learning|Filip Granqvist, Congzheng Song, Áine Cahill, Rogier van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan K...|2024-04-01|NeurIPS|https://github.com/apple/pfl-research.|http://papers.nips.cc/paper_files/paper/2024/hash/4c8c6de56ecdd05e61abcd9e057c6142-Abstract-Datasets_and_Benchmarks_Track.html| 385 | |258|CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data|Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Lu Yang, Yuan Xie, Yanyun Qu|2024-03-24|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/shijiangming1/CLIP2FL.|https://doi.org/10.1609/aaai.v38i13.29416| 386 | |259|Federated Learning via Input-Output Collaborative Distillation|Xuan Gong, Shanglin Li, Yuxiang Bao, Barry Yao, Yawen Huang, Ziyan Wu, Baochang Zhang, Yefeng Zheng, David S. Doermann|2024-03-24|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/lsl001006/FedIOD.|https://doi.org/10.1609/aaai.v38i20.30209| 387 | |260|An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning|Jianqing Zhang, Yang Liu, Hua Yang, Jian Cao|2024-03-23|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|https://github.com/TsingZ0/FedKTL|https://doi.org/10.1109/cvpr52733.2024.01151| 388 | |261|Basalt: Server-Client Joint Defense Mechanism for Byzantine-Robust Federated Learning|Anxiao Song, H. Li, Tao Zhang, Ke Cheng, Yulong Shen|2024-03-18|OpenAlex|https://github.com/NSS-01/Basalt-Federated-learning.git.|https://doi.org/10.36227/techrxiv.171073035.50327931/v1| 389 | |262|FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models|Younghan Lee, Yungi Cho, Woorim Han, Ho Bae, Yunheung Paek|2024-03-05|Lecture notes in computer science|https://github.com/201younghanlee/FLGuard|https://doi.org/10.1007/978-3-031-51482-1_4| 390 | |263|Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling|Xingyan Chen, Tian Du, Mu Wang, Tiancheng Gu, Yu Zhao, Gang Kou, Changqiao Xu, Dapeng Oliver Wu|2024-03-01|IEEE Transactions on Computers|https://github.com/elegy112138/FedCMD.|https://doi.org/10.48550/arXiv.2403.02360| 391 | |264|Text-Enhanced Data-free Approach for Federated Class-Incremental Learning|Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung|2024-03-01|arXiv|https://github.com/tmtuan1307/lander.|http://arxiv.org/abs/2403.14101v1| 392 | |265|Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos|Tianyi Zhang, Yu Cao, Dianbo Liu|2024-02-29|arXiv|https://github.com/destiny301/uefl.|https://openreview.net/forum?id=EU5lci90fF| 393 | |266|FedKit: Enabling Cross-Platform Federated Learning for Android and iOS|Shen He Shen He, Beilong Tang, Boyan Zhang, Jiaoqi Shao, Xiaomin Ouyang, Daniel Nata Nugraha, Bing Luo|2024-02-16|IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)|https://github.com/FedCampus/FedKit.|https://doi.org/10.1109/INFOCOMWKSHPS61880.2024.10620662| 394 | |267|FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing|Yongzhe Jia, Xuyun Zhang, Amin Beheshti, Wanchun Dou|2024-02-13|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/jyzgh/FedLPS.|https://doi.org/10.1609/aaai.v38i11.29181| 395 | |268|OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning|Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, Siheng Chen|2024-02-10|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/rui-ye/OpenFedLLM.|https://doi.org/10.48550/arXiv.2402.06954| 396 | |269|FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning|Jialuo He, Wei Chen, Xiaojin Zhang|2024-02-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/Gp1g/FedAA.|https://doi.org/10.1609/aaai.v39i16.33878| 397 | |270|Federated Learning with New Knowledge: Fundamentals, Advances, and Futures|Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu|2024-02-01|arXiv|https://github.com/conditionWang/FLNK.|https://doi.org/10.48550/arXiv.2402.02268| 398 | |271|Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off|Yuecheng Li, Lele Fu, Tong Wang, Jian Lou, Bin Chen, Lei Yang, Jian Shen, Zibin Zheng, Chuan Chen|2024-02-01|ICML|https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other.|https://openreview.net/forum?id=C7dmhyTDrx| 399 | |272|FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning|Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed, Atta Ur Rahman|2024-02-01|arXiv|https://github.com/SimuEnv/FedSiKD|https://doi.org/10.48550/arXiv.2402.09095| 400 | |273|FedGuCci: Making Local Models More Connected in Landscape for Federated Learning|Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Tao Shen, Tao Lin, Chao Wu, Nicholas D. Lane|2024-02-01|OpenAlex|https://github.com/ZexiLee/fedgucci|https://doi.org/10.1145/3711896.3737037| 401 | |274|FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning|Gongxi Zhu, Donghao Li, Hanlin Gu, Yuan Yao, Lixin Fan, Yuxing Han|2024-02-01|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|https://github.com/Liar-Mask/FedMIA.|https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html| 402 | |275|Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study|Yuwen Yang, Yuxiang Lu, Suizhi Huang, Shalayiding Sirejiding, Hongtao Lu, Yue Ding|2024-02-01|arXiv|https://github.com/youngfish42/FMTL-Benchmark|http://arxiv.org/abs/2402.12876v2| 403 | |276|MetaVers: Meta-Learned Versatile Representations for Personalized Federated Learning|Jin Hyuk Lim, SeungBum Ha, Sung Whan Yoon|2024-01-03|2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|https://github.com/eepLearning/MetaVers.|https://doi.org/10.1109/wacv57701.2024.00257| 404 | |277|Federated learning meets remote sensing|Sergio Moreno-Álvarez, Mercedes Eugenia Paoletti, A. J. Sanchez-Fernandez, Juan A. Rico-Gallego, Lirong Han, Juan Mario ...|2024-01-01|Expert Systems with Applications|https://github.com/hpc-unex/FLmeetsRS.|https://doi.org/10.1016/j.eswa.2024.124583| 405 | |278|Federated learning on non-IID and globally long-tailed data via meta re-weighting networks|Yang Lu, Pinxin Qian, Shanshan Yan, Gang Huang, Yuan Yan Tang|2024-01-01|International Journal of Wavelets Multiresolution and Information Processing|https://github.com/pxqian/FedReN|https://doi.org/10.1142/S0219691323500637| 406 | |279|Federation-Paced Learning: Towards Efficient Federated Learning with Synchronized Pace|Tingting Zhang, Mei Cao, Zhenge Jia, Jianbo Lu, Zhaoyan Shen, Dongxiao Yu, Mengying Zhao|2024-01-01|Frontiers in artificial intelligence and applications|https://github.com/tnghua/FedPL.|https://doi.org/10.3233/FAIA240722| 407 | |280|GAS: Generative Activation-Aided Asynchronous Split Federated Learning|Jiarong Yang, Yuan Liu|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/eejiarong/GAS.|https://doi.org/10.48550/arXiv.2409.01251| 408 | |281|Hierarchical Federated Learning with Multi-Timescale Gradient Correction|Wenzhi Fang, Dong-Jun Han, Evan Chen, Shiqiang Wang, Christopher G. Brinton|2024-01-01|NeurIPS|https://github.com/wenzhifang/MTGC|http://papers.nips.cc/paper_files/paper/2024/hash/8fb96e8d0fbf591b1fa1ad85653d8417-Abstract-Conference.html| 409 | |282|Improving Transferability of Network Intrusion Detection in a Federated Learning Setup|Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal|2024-01-01|OpenAlex|https://github.com/ghosh64/transferability.|https://doi.org/10.1109/icmlcn59089.2024.10624761| 410 | |283|RoseAgg: Robust Defense Against Targeted Collusion Attacks in Federated Learning|He Yang, Wei Xi, Yuhao Shen, Canhui Wu, Jizhong Zhao|2024-01-01|IEEE Transactions on Information Forensics and Security|https://github.com/SleepedCat/RoseAgg.|https://doi.org/10.1109/TIFS.2024.3352415| 411 | |284|Multi-Level Additive Modeling for Structured Non-IID Federated Learning|Shutong Chen, Tianyi Zhou, Guodong Long, Jie Ma, Jing Jiang, Chengqi Zhang|2024-01-01|arXiv|https://github.com/shutong043/FeMAM.|https://doi.org/10.48550/arXiv.2405.16472| 412 | |285|On the Efficiency of Privacy Attacks in Federated Learning|Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, Yanzhao Wu|2024-01-01|2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)|https://github.com/mlsysx/EPAFL.|https://doi.org/10.1109/CVPRW63382.2024.00426| 413 | |286|PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning|Haiyang Guo, Fei Zhu, Wenzhuo Liu, Xu-Yao Zhang, Cheng-Lin Liu|2024-01-01|arXiv|https://github.com/Ghy0501/PILoRA|http://arxiv.org/abs/2401.02094v2| 414 | |287|PraFFL: A Preference-Aware Scheme in Fair Federated Learning|Rongguang Ye, Wei-Bin Kou, Ming Tang|2024-01-01|OpenAlex|https://github.com/rG223/PraFFL.|https://doi.org/10.48550/arXiv.2404.08973| 415 | |288|Selective Aggregation for Low-Rank Adaptation in Federated Learning|Pengxin Guo, Shuang Zeng, Yanran Wang, Huijie Fan, Feifei Wang, Liangqiong Qu|2024-01-01|arXiv|https://github.com/Pengxin-Guo/FedSA-LoRA.|https://openreview.net/forum?id=iX3uESGdsO| 416 | |289|Tackling Data Heterogeneity in Federated Learning via Loss Decomposition|Shuang Zeng, Pengxin Guo, Shuai Wang, Jianbo Wang, Yuyin Zhou, Liangqiong Qu|2024-01-01|Lecture notes in computer science|https://github.com/Zeng-Shuang/FedLD.|https://doi.org/10.1007/978-3-031-72117-5_66| 417 | |290|Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems|Qiong Wu, Xiaobo Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Jiangzhou Wang|2024-01-01|arXiv|https://github.com/qiongwu86/Vehicle-selection-for-C-V2X.git|http://arxiv.org/abs/2401.07224v1| 418 | |291|Federated Learning Client Pruning for Noisy Labels|Mahdi Morafah, Hojin Chang, Chen Chen, Bill Lin|2024-01-01||https://github.com/MMorafah/ClipFL.|https://doi.org/10.48550/arXiv.2411.07391| 419 | |292|Federated Learning with Convex Global and Local Constraints|Chuan He, Le Peng, Ju Sun|2024-01-01|Trans. Mach. Learn. Res.|https://github.com/PL97/Constr_FL|https://openreview.net/forum?id=qItxVbWyfe| 420 | |293|FedPKR: Federated Learning With Non-IID Data via Periodic Knowledge Review in Edge Computing|Jinbo Wang, Ruijin Wang, Guangquan Xu, Donglin He, Xikai Pei, Fengli Zhang, Jie Gan|2024-01-01|IEEE Transactions on Sustainable Computing|https://github.com/jbwangnb/FedPKR|https://doi.org/10.1109/TSUSC.2024.3374049| 421 | |294|Federated Fairness Analytics: Quantifying Fairness in Federated Learning|Oscar Dilley, Juan Marcelo Parra Ullauri, Rasheed Hussain, Dimitra Simeonidou|2024-01-01|arXiv|https://github.com/oscardilley/federated-fairness.|https://doi.org/10.48550/arXiv.2408.08214| 422 | |295|EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning|Jingtao Li, Xing Chen, Li Yang, Adnan Siraj Rakin, Deliang Fan, Chaitali Chakrabarti|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/zlijingtao/SFL-MEA|https://doi.org/10.1609/aaai.v38i12.29258| 423 | |296|A New Federated Learning Framework Against Gradient Inversion Attacks|Pengxin Guo, Shuang Zeng, Wenhao Chen, Xiaodan Zhang, Weihong Ren, Yuyin Zhou, Liangqiong Qu|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/Pengxin-Guo/HyperFL.|https://doi.org/10.48550/arXiv.2412.07187| 424 | |297|Analytic Federated Learning|Huiping Zhuang, Run He, Kai Tong, Di Fang, Han Sun, Haoran Li, Tianyi Chen, Ziqian Zeng|2024-01-01|arXiv|https://github.com/ZHUANGHP/Analytic-federated-learning|https://doi.org/10.48550/arXiv.2405.16240| 425 | |298|BAFFLE: A Baseline of Backpropagation-Free Federated Learning|Haozhe Feng, Tianyu Pang, Chao‐Hai Du, Wei Chen, Shuicheng Yan, Min Lin|2024-01-01|Lecture notes in computer science|https://github.com/FengHZ/BAFFLE.|https://doi.org/10.1007/978-3-031-73226-3_6| 426 | |299|BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing|Lixiang Yuan, Mingxing Duan, Guoqing Xiao, Zhuo Tang, Kenli Li|2024-01-01|IEEE Transactions on Knowledge and Data Engineering|https://github.com/ylxzjy/BMFL.git|https://doi.org/10.1109/TKDE.2024.3372708| 427 | |300|BapFL: You can Backdoor Personalized Federated Learning|Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao|2024-01-01|ACM Transactions on Knowledge Discovery from Data|https://github.com/BapFL/code|https://doi.org/10.1145/3649316| 428 | |301|COALA: A Practical and Vision-Centric Federated Learning Platform|Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu|2024-01-01|ICML|https://github.com/SonyResearch/COALA.|https://openreview.net/forum?id=ATRnM8PyQX| 429 | |302|Continual Adaptation of Vision Transformers for Federated Learning|Shaunak Halbe, James Seale Smith, Junjiao Tian, Zsolt Kira|2024-01-01|Trans. Mach. Learn. Res.|https://github.com/shaunak27/hepco-fed.|https://openreview.net/forum?id=vsZ5A3Zxyr| 430 | |303|FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning|Jianqing Zhang, Yang Liu, Yang Hua, Jian Cao|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/TsingZ0/FedTGP.|https://doi.org/10.1609/aaai.v38i15.29617| 431 | |304|Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network|Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief|2024-01-01|arXiv|https://github.com/qiongwu86/Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning|http://arxiv.org/abs/2401.09886v2| 432 | |305|Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping|Jingjing Zheng, Kai Li, Xin Yuan, Wei Ni, Eduardo Tovar|2024-01-01|OpenAlex|https://github.com/jjzgeeks/GradCAM-AE|https://doi.org/10.1145/3589335.3651490| 433 | |306|Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains|Ke Hu, Liyao Xiang, Peng Tang, Weidong Qiu|2024-01-01|OpenAlex|https://github.com/LonelyMoonDesert/FNR-FL.|https://www.ijcai.org/proceedings/2024/457| 434 | |307|Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning|Jiayuan Zhang, Xuefeng Liu, Yukang Zhang, Guogang Zhu, Jianwei Niu, Shaojie Tang|2024-01-01|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/ZhangJiayuan-BUAA/FedTSA.|https://doi.org/10.1145/3637528.3671908| 435 | |308|Exploring Visual Explanations for Defending Federated Learning against Poisoning Attacks|Jingjing Zheng, Kai Li, Xin Yuan, Wei Ni, Eduardo Tovar, Jon Crowcroft|2024-01-01|Proceedings of the 28th Annual International Conference on Mobile Computing And Networking|https://github.com/jjzgeeks/LayerCAM-AE|https://doi.org/10.1145/3636534.3697430| 436 | |309|FedSheafHN: Personalized Federated Learning on Graph-structured Data|Wenfei Liang, Yanan Zhao, Rui She, Yiming Li, Wee Peng Tay|2024-01-01|arXiv|https://github.com/CarrieWFF/ICML-2024-submission-recording|https://doi.org/10.48550/arXiv.2405.16056| 437 | |310|FKD-Med: Privacy-Aware, Communication-Optimized Medical Image Segmentation via Federated Learning and Model Lightweighting Through Knowledge Distillation|Guanqun Sun, Han Shu, Feihe Shao, Teeradaj Racharak, Weikun Kong, Yizhi Pan, Jingjing Dong, Shuang Wang, Le-Minh Nguyen,...|2024-01-01|IEEE Access|https://github.com/SUN-1024/FKD-Med.|https://doi.org/10.1109/ACCESS.2024.3372394| 438 | |311|FedSarah: A Novel Low-Latency Federated Learning Algorithm for Consumer-Centric Personalized Recommendation Systems|Zhiguo Qu, Jian Ding, Rutvij H. Jhaveri, Youcef Djenouri, Xin Ning, Prayag Tiwari|2024-01-01|IEEE Transactions on Consumer Electronics|https://github.com/DashingJ-82/FedSarah.git.|https://doi.org/10.1109/TCE.2023.3342100| 439 | |312|FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data|Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu|2024-01-01|arXiv|https://github.com/ZackZikaiXiao/FedLoGe|https://openreview.net/forum?id=V3j5d0GQgH| 440 | |313|FedLF: Layer-Wise Fair Federated Learning|Zibin Pan, Chi Li, Fangchen Yu, Shuyi Wang, Haijin Wang, Xiaoying Tang, Zhao Jun-hua|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/zibinpan/FedLF.|https://doi.org/10.1609/aaai.v38i13.29368| 441 | |314|FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting|Shu‐Ling Cheng, Chin-Yuan Yeh, Ting‐An Chen, Eliana Pastor, Ming-Syan Chen⋆|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/celinezheng/fedgcr.|https://doi.org/10.1609/aaai.v38i10.29031| 442 | |315|Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning|Teppei Suzuki|2024-01-01|arXiv|https://github.com/DensoITLab/Fed3DGS|https://doi.org/10.48550/arXiv.2403.11460| 443 | |316|Exploring Vacant Classes in Label-Skewed Federated Learning|Kuangpu Guo, Yuhe Ding, Jian Liang, Ran He, Zilei Wang, Tieniu Tan|2024-01-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/krumpguo/FedVLS.|https://doi.org/10.1609/aaai.v39i16.33864| 444 | |317|FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch|Sunny Gupta, Mohit Jindal, Pankhi Kashyap, Pranav Jeevan, Amit Sethi|2024-01-01|2021 IEEE International Conference on Big Data (Big Data)|https://github.com/sunnyinAI/FLeNS|https://doi.org/10.1109/BigData62323.2024.10825820| 445 | |318|Disentangling Client Contributions: Improving Federated Learning Accuracy in the Presence of Heterogeneous Data|Chunming Liu, Daniyal M. Alghazzawi, Li Cheng, Gaoyang Liu, Chen Wang, Cheng Zeng, Yang Yang|2023-12-21|ISPA/BDCloud/SocialCom/SustainCom|https://github.com/ChunmingLiu23/FedVa.|https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00082| 446 | |319|Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging|Ziyuan Yang, Wenjun Xia, Zexin Lu, Ying-Yu Chen, Xiaoxiao Li, Yi Zhang|2023-12-15|IEEE Transactions on Neural Networks and Learning Systems|https://github.com/Zi-YuanYang/HyperFed.|https://doi.org/10.1109/tnnls.2023.3338867| 447 | |320|Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents|Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, Yiran Chen|2023-12-01|Lecture notes in computer science|https://github.com/FedDG23/FedDG-main.git|https://doi.org/10.1007/978-3-031-73229-4_2| 448 | |321|Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images|Bao Li, Zhenyu Liu, Lizhi Shao, Bensheng Qiu, Hong Bu, Jie Tian|2023-12-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/boyden/PointTransformerFL|https://doi.org/10.48550/arXiv.2312.06454| 449 | |322|Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts|Jiayi Chen, Benteng Ma, Hengfei Cui, Yong Xia|2023-12-01|arXiv|https://github.com/JiayiChen815/FEAL.|http://arxiv.org/abs/2312.02567v2| 450 | |323|SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks|Peishen Yan, Hao Wang, Tao Song, Yang Hua, Ruhui Ma, Ningxin Hu, Mohammad Reza Haghighat, Haibing Guan|2023-12-01|Lecture notes in computer science|https://github.com/KoalaYan/SkyMask.|https://doi.org/10.1007/978-3-031-72655-2_17| 451 | |324|PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning|Yuting Ma, Yuanzhi Yao, Xiaohua Xu|2023-12-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/ytingma/PPIDSG.|https://doi.org/10.48550/arXiv.2312.10380| 452 | |325|Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast|Guangyin Bao, Qi Zhang, Duoqian Miao, Zixuan Gong, Liang Hu, Ke Liu, Yang Liu, Chongyang Shi|2023-12-01|arXiv|https://github.com/BaoGuangYin/PmcmFL.|https://doi.org/10.48550/arXiv.2312.13508| 453 | |326|Federated Learning with Extremely Noisy Clients via Negative Distillation|Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang|2023-12-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/linChen99/FedNed.|https://doi.org/10.48550/arXiv.2312.12703| 454 | |327|FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels|Jichang Li, Guanbin Li, Hui Cheng, Zicheng Liao, Yizhou Yu|2023-12-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/lijichang/FLNL-FedDiv.|https://doi.org/10.48550/arXiv.2312.12263| 455 | |328|Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning|Reda Khoufache, Mustapha Lebbah, Hanene Azzag, Etienne Goffinet, Djamel Bouchaffra|2023-12-01|Society for Industrial and Applied Mathematics eBooks|https://github.com/redakhoufache/DisCGS.|https://doi.org/10.1137/1.9781611978032.93| 456 | |329|Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration|Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Quyang Pan, Tianliu He, Xuefeng Jiang|2023-12-01|IEEE INFOCOM 2022 - IEEE Conference on Computer Communications|https://github.com/wuzhiyuan2000/FedAgg.|https://doi.org/10.1109/INFOCOM52122.2024.10621254| 457 | |330|Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space|Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart|2023-12-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/hasanmohsin/betaPredBayesFL.|https://doi.org/10.48550/arXiv.2312.09817| 458 | |331|FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application|Md. Zarif Hossain, Ahmed Imteaj, Saika Zaman, Abdur R. Shahid, Sajedul Talukder, M. Hadi Amini|2023-11-07|OpenAlex|https://github.com/speedlab-git/FLID|https://doi.org/10.1109/dsc61021.2023.10354149| 459 | |332|SGFL: A Federated Learning Approach for Non-IID Data Using Semi-Supervised DCGAN|Alireza Rabiee, Abolfazl Ajdarloo, Mohsen Rahmani|2023-11-01|OpenAlex|https://github.com/apaliray03/SGFL.|https://doi.org/10.1109/iccke60553.2023.10326270| 460 | |333|Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark|Wenke Huang, Mang Ye, Zekun Shi, Guancheng Wan, He Li, Bo Du, Qiang Yang|2023-11-01|IEEE Transactions on Pattern Analysis and Machine Intelligence|https://github.com/WenkeHuang/MarsFL.|https://doi.org/10.1109/TPAMI.2024.3418862| 461 | |334|Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction|Shanghao Shi, Ning Wang, Yang Xiao, Chaoyu Zhang, Yi Shi, Y. Thomas Hou, Wenjing Lou|2023-11-01|OpenAlex|https://github.com/unknown123489/Scale-MIA.|https://www.ndss-symposium.org/ndss-paper/scale-mia-a-scalable-model-inversion-attack-against-secure-federated-learning-via-latent-space-reconstruction/| 462 | |335|A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective|Xianghua Xie, Chen Hu, Hanchi Ren, Jingjing Deng|2023-11-01|Neurocomputing|https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning.|https://doi.org/10.1016/j.neucom.2023.127225| 463 | |336|FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion|DaiXun Li, Weiying Xie, Yunsong Li, Leyuan Fang|2023-11-01|IEEE Transactions on Geoscience and Remote Sensing|https://github.com/LDXDU/FedFusion|https://doi.org/10.48550/arXiv.2311.09540| 464 | |337|Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence|Shu Zheng, Tiandi Ye, Xiang Li, Ming Gao|2023-11-01|ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|https://github.com/fedcome/fedcome.|https://doi.org/10.1109/ICASSP48485.2024.10446892| 465 | |338|Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces|Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava|2023-11-01|arXiv|https://github.com/apd10/FLCF|http://arxiv.org/abs/2311.01722v1| 466 | |339|Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning|Gergely Dániel Németh, Miguel Ángel Lozano, Novi Quadrianto, Nuria Oliver|2023-11-01|IEEE Access 13 (2025) 40258-40274|https://github.com/ellisalicante/ma-fl-mia|https://doi.org/10.1109/access.2025.3546478| 467 | |340|Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning|Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang|2023-11-01|ACM Trans. Inf. Syst. 42 (2024) 1 - 29|https://github.com/wjfu99/FL-epidemic.|http://arxiv.org/abs/2311.06049v1| 468 | |341|Federated learning for diagnosis of age-related macular degeneration|Sina Gholami, Jennifer I. Lim, Theodore Leng, Sally Shin Yee Ong, Atalie Carina Thampson, Minhaj Nur Alam|2023-10-12|bioRxiv (Cold Spring Harbor Laboratory)|https://github.com/QIAIUNCC/FL_UNCC_QIAI.|https://doi.org/10.3389/fmed.2023.1259017| 469 | |342|Federated Learning and Differential Privacy in AI-Based Surveillance Systems Model|Jason Adiwijaya, Venansius Reynardi Tanaya, Anderies, Andry Chowanda|2023-10-04|OpenAlex|https://github.com/slimmyYer211/RMCS-PPCV_01|https://doi.org/10.1109/icts58770.2023.10330863| 470 | |343|Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning|Feng Wang, Senem Velipasalar, Mustafa Cenk Gursoy|2023-10-01|2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)|https://github.com/wfwf10/MKOR.|https://doi.org/10.1109/wacv57701.2024.00384| 471 | |344|Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization|Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren, Jiwen Zhou, Victor S. Sheng, Huaiyu Dai, Dejing Dou|2023-10-01|Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing|https://github.com/llm-eff/FedPepTAO.|https://doi.org/10.18653/v1/2023.emnlp-main.488| 472 | |345|FedNAR: Federated Optimization with Normalized Annealing Regularization|Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang|2023-10-01||https://github.com/ljb121002/fednar|http://arxiv.org/abs/2310.03163v1| 473 | |346|FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning|Peiran Xu, Zeyu Wang, Jieru Mei, Liangqiong Qu, Alan L. Yuille, Cihang Xie, Yuyin Zhou|2023-10-01|Trans. Mach. Learn. Res.|https://github.com/UCSC-VLAA/FedConv|https://openreview.net/forum?id=bzTfO4mURl| 474 | |347|FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things|Samiul Alam, Tuo Zhang, Tiantian Feng, Hui Shen, Zhichao Cao, Dong Zhao, JeongGil Ko, Kiran Somasundaram, Shrikanth S. N...|2023-10-01|arXiv|https://github.com/AIoT-MLSys-Lab/FedAIoT.|https://doi.org/10.48550/arXiv.2310.00109| 475 | |348|FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models|Tao Fan, Yan Kang, Guoqiang Ma, Weijing Chen, Wenbin Wei, Lixin Fan, Qiang Yang|2023-10-01|arXiv|https://github.com/FederatedAI/FATE-LLM|https://doi.org/10.48550/arXiv.2310.10049| 476 | |349|Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies|Yongxin Guo, Xiaoying Tang, Tao Lin|2023-10-01|ICLR|https://github.com/LINs-lab/HCFL.|https://openreview.net/forum?id=zPDpdk3V8L| 477 | |350|Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping|Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman, Jan "Honza" Silovsky, Kunal Talwar, Christopher G. Brinton, Tatiana L...|2023-10-01|arXiv|https://github.com/apple/ml-pfl4asr.|http://arxiv.org/abs/2310.00098v2| 478 | |351|Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition|Sara Pieri, Jose Renato Restom, Samuel Horvath, Hisham Cholakkal|2023-10-01|arXiv|https://github.com/sarapieri/fed_het.git.|http://arxiv.org/abs/2310.15165v1| 479 | |352|RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation|Dzung L. Pham, S. H. Kulkarni, Amir Houmansadr|2023-10-01|OpenAlex|https://github.com/dzungvpham/raifle.|https://www.ndss-symposium.org/ndss-paper/raifle-reconstruction-attacks-on-interaction-based-federated-learning-with-adversarial-data-manipulation/| 480 | |353|ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning|Lu Wang, Hao Yu, Jindong Wang, Damien Teney, Haohan Wang, Yao Zhu, Yiqiang Chen, Qiang Yang, Xing Xie, Xiangyang Ji|2023-10-01|Lecture notes in computer science|https://github.com/microsoft/PersonalizedFL|https://doi.org/10.1007/978-3-031-82240-7_2| 481 | |354|Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory|Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota|2023-09-01|IEEE Access, vol. 12, pp. 139692-139710, September 2024|https://github.com/Masuyama-lab/FCAC|http://arxiv.org/abs/2309.03487v1| 482 | |355|Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning|Zebang Shen, Jiayuan Ye, A.N.C. Kang, Hamed Hassani, Reza Shokri|2023-09-01|ICLR 2023 poster|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/share-your-representation-only-guaranteed/code)|https://openreview.net/pdf/65d25b717d0c0bbcfc88e898afc2ffee03b7d15e.pdf| 483 | |356|Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness|Haoming Wang, Wei Gao|2023-09-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/pittisl/FL-with-intertwined-heterogeneity.|https://doi.org/10.1609/aaai.v39i20.35405| 484 | |357|Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration|Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang|2023-09-01|2021 IEEE/CVF International Conference on Computer Vision (ICCV)|https://github.com/kxzxvbk/Fling.|https://doi.org/10.1109/ICCV51070.2023.01775| 485 | |358|FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler|Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr Kindratenko, E. A. Huerta, Kibaek Kim,...|2023-09-01|arXiv|https://github.com/APPFL/FedCompass.|https://openreview.net/forum?id=msXxrttLOi| 486 | |359|Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments|Simon Queyrut, Valerio Schiavoni, Pascal Felber|2023-09-01|OpenAlex|https://github.com/queyrusi/Pelta.|https://doi.org/10.1109/ICDCS57875.2023.00069| 487 | |360|FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization|Qianyu Long, Christos Anagnostopoulos, Shameem Puthiya Parambath, Daning Bi|2023-09-01|2021 IEEE International Conference on Data Mining (ICDM)|https://github.com/EricLoong/feddip.|https://doi.org/10.1109/ICDM58522.2023.00146| 488 | |361|FedJudge: Federated Legal Large Language Model|Linan Yue, Qi Liu, Yichao Du, Weibo Gao, Ye Liu, Fangzhou Yao|2023-09-01|arXiv|https://github.com/yuelinan/FedJudge.|http://arxiv.org/abs/2309.08173v3| 489 | |362|FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning|Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingre...|2023-09-01|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/alibaba/FederatedScope|https://doi.org/10.48550/arXiv.2309.00363| 490 | |363|Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective|Zhen Qin, Feiyi Chen, Chen Zhi, Xueqiang Yan, Shuiguang Deng|2023-09-01|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/zhenqincn/Snowball|https://doi.org/10.48550/arXiv.2309.16456| 491 | |364|Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications|Neveen Mohammad Hijazi, Moayad Aloqaily, Mohsen Guizani, Bassem Ouni, Fakhri Karray|2023-08-04|IEEE Internet of Things Journal|https://github.com/Artifitialleap-MBZUAI/Secure-Federated-Learning-with-Fully-Homomorphic-Encryption-for-IoT-Communications|https://doi.org/10.1109/jiot.2023.3302065| 492 | |365|Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification|Ziyuan Yang, Andrew Beng Jin Teoh, Bob Zhang, Lu Leng, Yi Zhang|2023-08-01|International Journal of Computer Vision|https://github.com/Zi-YuanYang/PSFed-Palm.|https://doi.org/10.1007/s11263-024-02077-9| 493 | |366|Understanding the Role of Layer Normalization in Label-Skewed Federated Learning|Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen|2023-08-01|Trans. Mach. Learn. Res.|https://github.com/huawei-noah/Federated-Learning|https://openreview.net/forum?id=6BDHUkSPna| 494 | |367|Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment|Felix Wagner, Zeju Li, Pramit Saha, Konstantinos Kamnitsas|2023-08-01|Lecture notes in computer science|https://github.com/FelixWag/StarAlign|https://doi.org/10.1007/978-3-031-45676-3_26| 495 | |368|Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search|Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang|2023-08-01|arXiv|https://github.com/ternencewu123/GAutoMRI.|http://arxiv.org/abs/2308.13995v1| 496 | |369|Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning|Yun-Hin Chan, Rui Zhou, Running Zhao, Zhihan Jiang, Edith C. -H. Ngai|2023-08-01|arXiv|https://github.com/ChanYunHin/InCo-Aggregation|https://openreview.net/forum?id=Cc0qk6r4Nd| 497 | |370|Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment|Lucia Morris, Tori Qiu, Nikhil Raghuraman|2023-08-01|Neurips 2022 SyntheticData4ML|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/federated-learning-on-patient-data-for/code)|https://openreview.net/pdf/e1a42558c9d0898a8634d6e40f0f4b54c0c56310.pdf| 498 | |371|FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection|Naif Alkhunaizi, Koushik Srivatsan, Faris Almalik, Ibrahim Almakky, Karthik Nandakumar|2023-08-01|arXiv|https://github.com/Naiftt/FedSIS|http://arxiv.org/abs/2308.10236v2| 499 | |372|FedPop: Federated Population-based Hyperparameter Tuning|Haokun Chen, Denis Krompass, Jindong Gu, Volker Tresp|2023-08-01|arXiv|https://github.com/HaokunChen245/FedPop|http://arxiv.org/abs/2308.08634v3| 500 | |373|FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning|Guangyu Sun, Matias Mendieta, Jun Luo, Shandong Wu, Chen Chen|2023-08-01|2021 IEEE/CVF International Conference on Computer Vision (ICCV)|https://github.com/imguangyu/FedPerfix|https://doi.org/10.1109/ICCV51070.2023.00460| 501 | |374|Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data|Zhuang Qi, Lei Meng, Zitan Chen, Han Hu, Hui Lin, Xiangxu Meng|2023-08-01|ACM Multimedia|https://github.com/qizhuang-qz/FedCSPC.|https://doi.org/10.48550/arXiv.2308.03457| 502 | |375|ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data|Po‐Chuan Wang, Chen Shen, Weichung Wang, Masahiro Oda, Chiou‐Shann Fuh, Kensaku Mori, Holger R. Roth|2023-08-01|Lecture notes in computer science|https://github.com/NVIDIA/NVFlare|https://doi.org/10.1007/978-3-031-47401-9_30| 503 | |376|CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction|Umar Khalid, Hasan Iqbal, Saeed Vahidian, Jing Hua, Chen Chen|2023-08-01|2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|https://github.com/umarkhalidAI/CEFHRI-Efficient-Federated-Learning.|https://doi.org/10.1109/IROS55552.2023.10341467| 504 | |377|ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations|Xinpeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, Zhili Chen|2023-08-01|OpenAlex|https://github.com/KnightWan/ALI-DPFL.|https://doi.org/10.1109/WoWMoM60985.2024.00062| 505 | |378|FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence|Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Ke Xu, Wen Wang, Xuefeng Jiang, Bo Gao, Jinda Lu|2023-08-01|IEEE Transactions on Mobile Computing|https://github.com/wuzhiyuan2000/FedCache.|https://doi.org/10.36227/techrxiv.23255420.v4| 506 | |379|Brain Age Estimation Using Structural MRI: A Clustered Federated Learning Approach|Seyyed Saeid Cheshmi, Abtin Mahyar, Anita Soroush, Zahra Rezvani, Bahar J. Farahani|2023-07-23|OpenAlex|https://github.com/Abtinmy/Clustered-FL-BrainAGE.|https://doi.org/10.1109/coins57856.2023.10189329| 507 | |380|Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives|Moming Duan, Qinbin Li, Linshan Jiang, Bingsheng He|2023-07-01|arXiv|https://github.com/morningD/Towards-Open-Federated-Learning-Platforms-Survey|https://doi.org/10.48550/arXiv.2307.02140| 508 | |381|A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design|Mahdi Morafah, Weijia Wang, Bill Lin|2023-07-01|IEEE Transactions on Artificial Intelligence|https://github.com/MMorafah/FedZoo-Bench.|https://doi.org/10.48550/arXiv.2307.15245| 509 | |382|Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging|Meirui Jiang, Yuan Zhong, Anjie Le, Xiaoxiao Li, Qi Dou|2023-07-01|arXiv|https://github.com/med-air/Client-DP-FL.|http://arxiv.org/abs/2307.12542v2| 510 | |383|FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy|Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan|2023-07-01|Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|https://github.com/TsingZ0/FedCP.|https://doi.org/10.48550/arXiv.2307.01217| 511 | |384|FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation|Ming‐Hui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li|2023-07-01|Lecture notes in computer science|https://github.com/ubc-tea/FedSoup.|https://doi.org/10.1007/978-3-031-43895-0_30| 512 | |385|Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification|Marawan Elbatel, Hualiang Wang, Robert Martí, Huazhu Fu, Xiaomeng Li|2023-07-01|arXiv|https://github.com/xmed-lab/Fed-MAS|http://arxiv.org/abs/2307.14959v1| 513 | |386|Heterogeneous Federated Learning: State-of-the-art and Research Challenges|Mang Ye, Xiuwen Fang, Bo Du, Pong C. Yuen, Dacheng Tao|2023-07-01|ACM Computing Surveys|https://github.com/marswhu/HFL_Survey.|https://doi.org/10.48550/arXiv.2307.10616| 514 | |387|Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning|Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett|2023-07-01|NeurIPS 2023 (Datasets & Benchmarks)|https://github.com/google-research/dataset_grouper|http://arxiv.org/abs/2307.09619v2| 515 | |388|FedALA: Adaptive Local Aggregation for Personalized Federated Learning|Jianqing Zhang, Hua Yang, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan|2023-06-26|Proceedings of the AAAI Conference on Artificial Intelligence|https://github.com/TsingZ0/FedALA.|https://doi.org/10.48550/arXiv.2212.01197| 516 | |389|Stochastic Clustered Federated Learning|Dun Zeng, Xiangjing Hu, SHIYU LIU, Yue Yu, Hui Wang, Qifan Wang, Zenglin Xu|2023-06-25|FL4Data-Mining Poster|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/stochastic-clustered-federated-learning/code)|https://openreview.net/pdf/651da369c542d3ccb16cc3e79c1c05909c4d2860.pdf| 517 | |390|Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning|Junyi Zhu, Ruicong Yao, Matthew B. Blaschko|2023-06-01|ICML|https://github.com/JunyiZhu-AI/surrogate_model_extension.|https://proceedings.mlr.press/v202/zhu23m.html| 518 | |391|A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation|Ronast Subedi, Rebati Raman Gaire, Sharib Ali, Anh‐Tu Nguyen, Danail Stoyanov, Binod Bhattarai|2023-06-01|Lecture notes in computer science|https://github.com/thetna/distributed-da|https://doi.org/10.1007/978-3-031-44992-5_3| 519 | |392|FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling|Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky, Karthik Nandakumar|2023-06-01|arXiv|https://github.com/faresmalik/FeSViBS|http://arxiv.org/abs/2306.14638v1| 520 | |393|Personalized Federated Learning with Feature Alignment and Classifier Collaboration|Jian Xu, Xinyi Tong, Shao-Lun Huang|2023-06-01|ICLR 2023 notable top 5%|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/personalized-federated-learning-with-feature/code)|https://openreview.net/pdf/7e45d7414cae758349f97df5277f8897ef7b8c04.pdf| 521 | |394|Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network|Fan Liu, Siqi Lai, Yansong Ning, Hao Liu|2023-06-01|arXiv|https://github.com/usail-hkust/BkdFedGCN.|http://arxiv.org/abs/2306.10351v1| 522 | |395|FedNoisy: Federated Noisy Label Learning Benchmark|Siqi Liang, Jintao Huang, Junyuan Hong, Dun Zeng, Jiayu Zhou, Zenglin Xu|2023-06-01|arXiv|https://github.com/SMILELab-FL/FedNoisy|http://arxiv.org/abs/2306.11650v4| 523 | |396|FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning|Rishub Tamirisa, Chulin Xie, Wenxuan Bao, Andy Zhou, Ron Arel, Aviv Shamsian|2023-06-01|arXiv|https://github.com/lapisrocks/fedselect.|https://doi.org/10.1109/CVPR52733.2024.02264| 524 | |397|Federated Few-shot Learning|Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li|2023-06-01|arXiv|https://github.com/SongW-SW/F2L.|http://arxiv.org/abs/2306.10234v3| 525 | |398|Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs|Tengfei Ma, Trong Nghia Hoang, Jie Chen|2023-06-01|Springer optimization and its applications|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/federated-learning-of-models-pre-trained-on/code)|https://openreview.net/pdf/62b491564fe57a93617198b023b0a29ed9985a2b.pdf| 526 | |399|Improving Federated Aggregation with Deep Unfolding Networks|Shanika I Nanayakkara, Shiva Raj Pokhrel, Gang Li|2023-06-01|arXiv|https://github.com/shanikairoshi/Improved_DUN_basedFL_Aggregation|http://arxiv.org/abs/2306.17362v1| 527 | |400|Medical Federated Model with Mixture of Personalized and Sharing Components|Yawei Zhao, Qinghe Liu, Xinwang Liu, Kunlun He|2023-06-01|arXiv|https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.|http://arxiv.org/abs/2306.14483v1| 528 | |401|Masked Autoencoders are Parameter-Efficient Federated Continual Learners|Subarnaduti Paul, Lars-Joel Frey, Roshni Kamath, Kristian Kersting, Martin Mundt|2023-06-01|arXiv|https://github.com/ycheoo/pMAE.|http://arxiv.org/abs/2306.03542v2| 529 | |402|Partial Disentanglement with Partially-Federated GANs (PaDPaF)|Abdulla Jasem Almansoori, Samuel Horváth, Martin Takáč|2023-05-15|FLSys 2023|[![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/partial-disentanglement-with-partially/code)|https://openreview.net/pdf/1ead52c3348026844a31845c2754993bd02d259d.pdf| 530 | --------------------------------------------------------------------------------