├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Zhiyong Wang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 联邦学习 Federated Learning 2 | 3 | [![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT) 4 | [![LICENSE](https://img.shields.io/badge/license-Anti%20996-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE) [![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu) 5 | 6 | 7 | Everything about federated learning. *Your contribution is highly valued!* 8 | 9 | 关于联邦学习的资料，包括：介绍、综述文章、最新文章、代表工作及其代码、数据集、论文等等。 *欢迎一起贡献！* 10 | 11 | --- 12 | 13 | - 目录 14 | - [1. 教程 Tutorial](#1-教程-Tutorial) 15 | - [2. 相关论文 Related Papers](#2-相关论文-Related-Papers) 16 | - [3. 项目 Project](#3-项目-Project) 17 | - [4. 相关学者 Related Scholars](#4-相关学者-Related-Scholars) 18 | 19 | --- 20 | 21 | ## 1. 教程 Tutorial 22 | 23 | - 文字 24 | - [杨强：联邦学习](https://mp.weixin.qq.com/s/5FTrG5SZey2yeIbuyT3HoQ) 25 | - [Google - Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) 26 | - [联邦学习的研究及应用](https://mp.weixin.qq.com/s?src=11×tamp=1555896266&ver=1561&signature=ZtLlc7qakNAdw8hV3dxaB30PxtK9hAshYsIxccFf-D4eJrUw6YKQcqD0lD3SDMEn4egQTafUZr429er7SueP6HKLTr*uFKfr6JuHc3OvfdJ-uExiEJStHFynC65htbLp&new=1) 27 | - [杨强：GDPR对AI的挑战和基于联邦迁移学习的对策](https://zhuanlan.zhihu.com/p/42646278) 28 | 29 | - PPT 30 | - [联邦学习的研究与应用](https://aisp-1251170195.file.myqcloud.com/fedweb/1553845987342.pdf) 31 | - [Federated Learning and Transfer Learning for Privacy, Security and Confidentiality](https://aisp-1251170195.file.myqcloud.com/fedweb/1552916850679.pdf) (AAAI-19) 32 | - [GDPR, Data Shortage and AI](https://aisp-1251170195.file.myqcloud.com/fedweb/1552916659436.pdf) (AAAI-19) 33 | 34 | - 视频 35 | - [GDPR, Data Shortage and AI](https://aaai.org/Conferences/AAAI-19/invited-speakers/#yang) (AAAI-19 Invited Talk) 36 | 37 | - 新闻 38 | - 2019/02/09 [谷歌发布全球首个产品级移动端分布式机器学习系统，数千万手机同步训练](https://www.jiemian.com/article/2853096.html) 39 | 40 | --- 41 | 42 | ## 2. 相关论文 Related Papers 43 | 44 | - 综述与介绍 Survey And Introduction 45 | - arXiv 201912 - [Advances and Open Problems in Federated Learning](https://arxiv.org/abs/1912.04977) 58位学者联名综述 46 | - TIST 201902 - [Federated Machine Learning: Concept and Applications](https://dl.acm.org/citation.cfm?id=3298981) 47 | - arXiv 201909 - [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://arxiv.org/abs/1909.11875) 48 | - 应用 Application 49 | - 2019 - [Federated Learning for Mobile Keyboard Prediction](https://arxiv.org/abs/1811.03604) - Google将联邦学习用于自家输入法 50 | - 2019 - [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046) - Google千万设备级联邦学习系统设计 51 | - 联邦学习的提出 52 | - 2015 - [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://arxiv.org/abs/1511.03575) 53 | - 2016 - [Practical Secure Aggregation for Federated Learning on User-Held Data](https://arxiv.org/abs/1611.04482) 54 | - 2016 - [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://arxiv.org/abs/1610.02527) 55 | - 2017 - [Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/abs/1610.05492) 56 | - 2017 - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629) 联邦平均算法 the FederatedAveraging algorithm 57 | - 联邦学习安全性 58 | - NIPS 2016 - [Practical Secure Aggregation for Federated Learning on User-Held Data](https://arxiv.org/abs/1611.04482) 增强联邦学习的隐私保护能力 59 | - 2017 - [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557) 使用差分隐私避免泄露用户的贡献度 60 | - 2018 - [How to Backdoor Federated Leraning](https://arxiv.org/abs/1807.00459) Model Poisoning攻击 61 | - 2019 - [Can You Really Backdoor Federated Learning](https://arxiv.org/abs/1911.07963) 如何避免联邦学习被后门攻击 62 | - ICML 2019 - [Analyzing Federated Learning through an Adversarial Lens](https://arxiv.org/abs/1811.12470) Model Poisoning攻击 63 | - ICLR 2020 - [DBA: Distributed Backdoor Attacks against Federated Learning](https://openreview.net/forum?id=rkgyS0VFvr) Model Poisoning攻击，在两个最新鲁棒FL框架上验证 64 | - AAAI 2020 - [Robust Federated Training via Collaborative Machine Teaching using Trusted Instances](https://arxiv.org/abs/1905.02941) 鲁棒FL方法，诊断训练集中的Bugs和调整label. 65 | - 联邦学习扩展(FL+) 66 | - NIPS 2017 - [Federated Multi-Task Learning](http://papers.nips.cc/paper/7029-federated-multi-task-learning) 联邦多任务学习 67 | - arXiv 201901 - [Federated Reinforcement Learning](https://arxiv.org/abs/1901.08277) 联邦学习 + 强化学习 (Federated Learning + Reinforcement Learning) 68 | - arXiv 201901 - [SecureBoost: A Lossless Federated Learning Framework](https://arxiv.org/abs/1901.08755) 纵向联邦学习 (Vertical Federated Learning) 使用分布式决策树 69 | - arXiv 201810 - [Secure Federated Transfer Learning](https://arxiv.org/abs/1812.03337) 联邦迁移学习 70 | - ICML 2019 - [Bayesian Nonparametric Federated Learning of Neural Networks](https://arxiv.org/abs/1905.12022) 贝叶斯联邦学习 71 | - ICLR 2020 - [Federated Adversarial Domain Adaptation](https://arxiv.org/abs/1911.02054) 联邦对抗域适应 72 | - ICLR 2021 - [TOWARDS CAUSAL FEDERATED LEARNING FOR ENHANCED ROBUSTNESS AND PRIVACY] (https://arxiv.org/pdf/2104.06557.pdf) (Federated Learning + Causal Learning) 73 | - CyberC 2019 - [Record and Reward Federated Learning Contributions with Blockchain](https://mblocklab.com/RecordandReward.pdf) (Federated Learning + Blockchain) 74 | 75 | - 高效联邦学习 76 | - 2018 - [Expanding the Reach of Federated Leraning by Reducing Client Resource Requirements](https://arxiv.org/abs/1812.07210) 提出两个策略来提高通信效率 77 | - 2019 - [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://arxiv.org/abs/1903.02891) 提出压缩框架STC，可以减少训练时间和通信代价 78 | 79 | --- 80 | 81 | ## 3. 项目 Project 82 | 83 | - [FATE - 微众银行](https://github.com/WeBankFinTech/FATE) 84 | - [TensorFlow Federated](https://github.com/tensorflow/federated) 85 | - [Federated-Learning](https://github.com/roxanneluo/Federated-Learning) : An implement of google's paper. 86 | 87 | --- 88 | 89 | ## 4. 相关学者 Related Scholars 90 | 91 | - [杨强 Yang Qiang](https://scholar.google.com/citations?hl=en&user=1LxWZLQAAAAJ) 92 | - [H. Brendan McMahan](https://scholar.google.com/citations?user=iKPWydkAAAAJ&hl=en) 93 | - [jakub konečný](https://scholar.google.com/citations?user=4vq7eXQAAAAJ&hl=en) 94 | 95 | --- 96 | 97 | ## Contributors 98 | 99 | Thanks goes to these wonderful people: 100 | 101 | 102 | 103 | | [
王智勇(Wang Zhiyong)](https://github.com/ZeroWangZY)
| [
刘一璟(Liu Yijing)](https://github.com/zyplanet)
| 104 | | :---: | :---: | 105 | 106 | ## 欢迎参与贡献 107 | --------------------------------------------------------------------------------