Everything about federated learning. Your contribution is highly valued!
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！
-
文字
-
PPT
-
视频
- GDPR, Data Shortage and AI (AAAI-19 Invited Talk)
-
新闻
- 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
- 综述与介绍 Survey And Introduction
- arXiv 201912 - Advances and Open Problems in Federated Learning 58位学者联名综述
- TIST 201902 - Federated Machine Learning: Concept and Applications
- arXiv 201909 - Federated Learning in Mobile Edge Networks: A Comprehensive Survey
- 应用 Application
- 2019 - Federated Learning for Mobile Keyboard Prediction - Google将联邦学习用于自家输入法
- 2019 - Towards Federated Learning at Scale: System Design - Google千万设备级联邦学习系统设计
- 联邦学习的提出
- 2015 - Federated Optimization:Distributed Optimization Beyond the Datacenter
- 2016 - Practical Secure Aggregation for Federated Learning on User-Held Data
- 2016 - Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- 2017 - Federated Learning: Strategies for Improving Communication Efficiency
- 2017 - Communication-Efficient Learning of Deep Networks from Decentralized Data 联邦平均算法 the FederatedAveraging algorithm
- 联邦学习安全性
- NIPS 2016 - Practical Secure Aggregation for Federated Learning on User-Held Data 增强联邦学习的隐私保护能力
- 2017 - Differentially Private Federated Learning: A Client Level Perspective 使用差分隐私避免泄露用户的贡献度
- 2018 - How to Backdoor Federated Leraning Model Poisoning攻击
- 2019 - Can You Really Backdoor Federated Learning 如何避免联邦学习被后门攻击
- ICML 2019 - Analyzing Federated Learning through an Adversarial Lens Model Poisoning攻击
- ICLR 2020 - DBA: Distributed Backdoor Attacks against Federated Learning Model Poisoning攻击，在两个最新鲁棒FL框架上验证
- AAAI 2020 - Robust Federated Training via Collaborative Machine Teaching using Trusted Instances 鲁棒FL方法，诊断训练集中的Bugs和调整label.
- 联邦学习扩展(FL+)
- NIPS 2017 - Federated Multi-Task Learning 联邦多任务学习
- arXiv 201901 - Federated Reinforcement Learning 联邦学习 + 强化学习 (Federated Learning + Reinforcement Learning)
- arXiv 201901 - SecureBoost: A Lossless Federated Learning Framework 纵向联邦学习 (Vertical Federated Learning) 使用分布式决策树
- arXiv 201810 - Secure Federated Transfer Learning 联邦迁移学习
- ICML 2019 - Bayesian Nonparametric Federated Learning of Neural Networks 贝叶斯联邦学习
- ICLR 2020 - Federated Adversarial Domain Adaptation 联邦对抗域适应
- 高效联邦学习
- 2018 - Expanding the Reach of Federated Leraning by Reducing Client Resource Requirements 提出两个策略来提高通信效率
- 2019 - Robust and Communication-Efficient Federated Learning from Non-IID Data 提出压缩框架STC，可以减少训练时间和通信代价
- FATE - 微众银行
- TensorFlow Federated
- Federated-Learning : An implement of google's paper.
Thanks goes to these wonderful people:
 王智勇(Wang Zhiyong) |
 刘一璟(Liu Yijing) |
|---|