└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Vertical-Federated-Split-Learning 2 | 3 | ## 2021 Conference or Journal 4 | Title | Publication | Team/Authors | Methods 5 | :------: | :------: | :------: | :------: 6 | [Unleashing the tiger: Inference attacks on split learning](https://arxiv.org/abs/2012.02670) | CCS |EPFL/George Mason University| 7 | [VF2Boost: Very fast vertical federated gradient boosting for cross-enterprise learning](https://dl.acm.org/doi/abs/10.1145/3448016.3457241) | SIGMOD| Peking University/Tencent| 8 | [Label inference attacks against vertical federated learning](https://nesa.zju.edu.cn/download/fc_pdf_label_infer.pdf) | USENIX Security |Zhejiang University| 9 | [CAFE: Catastrophic data leakage in vertical federated learning](https://arxiv.org/abs/2110.15122) | NeurIPS|Rensselaer Polytechnic Institute/IBM Research | 10 | [AsySQN: Faster vertical federated learning algorithms with better computation resource utilization](https://dl.acm.org/doi/abs/10.1145/3447548.3467169) | KDD| Xidian University & JD Tech | 11 | [Feature inference attack on model predictions in vertical federated learning](https://arxiv.org/abs/2010.10152) | IEEE ICDE | National University of Singapore | 12 | [Secure bilevel asynchronous vertical federated learning with backward updating](https://ojs.aaai.org/index.php/AAAI/article/view/17301) | AAAI | Xidian University | 13 | [Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning](https://arxiv.org/abs/1812.03288) | TNNLS | Mohamed bin Zayed University of Artificial Intelligence/Xidian University | 14 | [Large-scale Secure XGB for Vertical Federated Learning](https://dl.acm.org/doi/abs/10.1145/3459637.348236) | CIKM | Ant Group | 15 | [Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm](https://dl.acm.org/doi/pdf/10.1145/3459637.3482249) | CIKM | Xidian University| 16 | [Defending against Reconstruction Attack in Vertical Federated Learning](https://fl-icml.github.io/2021/papers/FL-ICML21_paper_21.pdf) | ICML Workshop | Bytedance | 17 | [Vertical Federated Learning for Higher-Order Factorization Machines](https://link.springer.com/chapter/10.1007/978-3-030-75765-6_28/) | PAKDD |Hokkaido University | 18 | [Multi-tier federated learning for vertically partitioned data](https://link.springer.com/chapter/10.1007/978-3-030-75765-6_28/) | ICASSP |Rensselaer Polytechnic Institute| 19 | [Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study](https://medinform.jmir.org/2021/6/e26598/) | JMIR Med. Inform. | Yonsei University College of Medicine | 20 | 21 | ## 2021 Arxiv 22 | Title | Team/Authors | Methods 23 | :------: | :------: | :------: 24 | [Achieving Model Fairness in Vertical Federated Learning](https://arxiv.org/abs/2109.08344) | University of Victoria/Huawei | 25 | [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://arxiv.org/abs/2108.11444) | University of Surrey | 26 | [A Vertical Federated Learning Framework for Horizontally Partitioned Labels](https://arxiv.org/abs/2106.10056) | Peking University | 27 | [Exploiting Record Similarity for Practical Vertical Federated Learning](https://arxiv.org/pdf/2106.06312.pdf) | National University of Singapore | 28 | [A Vertical Federated Learning Framework for Graph Convolutional Network](https://arxiv.org/abs/2106.11593) | Ant Group | 29 | 30 | ## 2020 Conference or Journal 31 | Title | Publication | Team/Authors | Methods 32 | :------: | :------: | :------: | :------: 33 | [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://dl.acm.org/doi/abs/10.1145/3394486.3403298) | KDD | JD Finance America Corporation| 34 | [Privacy Preserving Vertical Federated Learning for Tree-based Models](http://www.vldb.org/pvldb/vol13/p2090-wu.pdf) | VLDB Journal | National University of Singapore/Zhejiang University| 35 | [Federated learning for vision-and-language grounding problems](https://ojs.aaai.org/index.php/AAAI/article/view/6824) | AAAI | Peking University/Tencent| 36 | [Split Deep Q-Learning for Robust Object Singulation](https://ieeexplore.ieee.org/abstract/document/9196647) | ICRA | Aristotle University of Thessaloniki| 37 | [VAFL: a Method of Vertical Asynchronous Federated Learning](https://arxiv.org/abs/2007.06081) | ICML Workshop | Rensselaer Polytechnic Institute/UCLA | 38 | [FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training](https://arxiv.org/abs/2008.10838v1) | IJCAI Workshop | WeBank| 39 | [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://ieeexplore.ieee.org/abstract/document/9252066) | SRDS | Data61, CSIRO| 40 | [Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://ieeexplore.ieee.org/abstract/document/9026781) | IEEE Communications Letters | Kyoto University| 41 | [Privacy-Sensitive Parallel Split Learning](https://ieeexplore.ieee.org/abstract/document/9016486) | ICOIN | Chung-Ang University| 42 | [Combining split and federated architectures for efficiency and privacy in deep learning](https://dl.acm.org/doi/abs/10.1145/3386367.3431678) | CONEXT | Saint Louis University| 43 | 44 | ## 2020 Arxiv 45 | Title | Team/Authors | Methods 46 | :------: | :------: | :------: 47 | [Privacy Leakage of Real-World Vertical Federated Learning](https://arxiv.org/abs/2011.09290) | Ant Group/Zhejiang University | 48 | --------------------------------------------------------------------------------