Federated Learning

Part 1: Introduction

Part 2: Survey

Part 3: Benchmarks

Part 4: Converge

4.1 Model Aggregation

4.2 Convergence Research

4.3 Statistical Heterogeneity

4.4 Adaptive Aggregation

Part 5: Security

5.1 Adversarial Attacks

5.2 Data Privacy and Confidentiality

Part 6: Communication Efficiency

6.1 Compression

Part 7: Personalized Federated Learning

7.1 Meta Learning

7.2 Multi-task Learning

7.3 Hierarchical FL

7.4 Transfer Learning

Part 8 Decentralization & Incentive Mechanism

8.1 Decentralized

8.2 Incentive Mechanism

Part 9: Vertical Federated Learning

Part 10: Wireless Communication and Cloud Computing

Part 11: Federated with Deep learning

11.1 Neural Architecture Search(NAS)

11.2 Graph Neural Network(GNN)

Part 12: FL system & Library & Courses

12.1 System

12.2 Courses

13.2 Secret Sharing

Part 13: Secure Multi-party Computation(MPC)

13.1 Differential Privacy

13.2 Secret Sharing

Part 14: Applications

14.1 Healthcare

14.2 Natual Language Processing

Google

Snips

14.3 Computer Vision

14.4 Recommendation

14.5 Industrial

Part 15: Organizations and Companies

15.1 国内篇

微众银行开源 FATE 框架.

Qiang Yang, Tianjian Chen, Yang Liu, Yongxin Tong.

字节跳动开源 FedLearner 框架.

Jiankai Sun, Weihao Gao, Hongyi Zhang, Junyuan Xie.《Label Leakage and Protection in Two-party Split learning》

华控清交 PrivPy 多方计算平台

Yi Li, Wei Xu.《PrivPy: General and Scalable Privacy-Preserving Data Mining》

同盾科技 同盾志邦知识联邦平台

Hongyu Li, Dan Meng, Hong Wang, Xiaolin Li.

百度 MesaTEE 安全计算平台

Tongxin Li, Yu Ding, Yulong Zhang, Tao Wei.《gbdt-rs: Fast and Trustworthy Gradient Boosting Decision Tree》

矩阵元 Rosetta 隐私开源框架
百度 PaddlePaddle 开源联邦学习框架
蚂蚁区块链科技 蚂蚁链摩斯安全计算平台
阿里云 DataTrust 隐私增强计算平台
百度百度点石联邦学习平台
富数科技 阿凡达安全计算平台
香港理工大学

《FedVision: An Online Visual Object Detection Platform Powered by Federated Learning》

《BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning》

《Abnormal Client Behavior Detection in Federated Learning》

北京航空航天大学

《Federated machine learning: Concept and applications》

《Failure Prediction in Production Line Based on Federated Learning: An Empirical Study》

15.2 国际篇

Google 提出 Federated Learning. H. Brendan McMahan. Daniel Ramage. Jakub Konečný. Kallista A. Bonawitz. Hubert Eichner.

《Communication-efficient learning of deep networks from decentralized data》

《Federated Learning: Strategies for Improving Communication Efficiency》

《Advances and Open Problems in Federated Learning》

《Towards Federated Learning at Scale: System Design》

《Differentially Private Learning with Adaptive Clipping》

......(更多联邦学习相关文章请自行搜索 Google Scholar)

Cornell University.

Antonio Marcedone.

《Practical Secure Aggregation for Federated Learning on User-Held Data》

《Practical Secure Aggregation for Privacy-Preserving Machine Learning》

Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov.

《How To Backdoor Federated Learning》

《Differential privacy has disparate impact on model accuracy》

Ziteng Sun.

《Can you really backdoor federated learning?》