LifangHe / federated-learning

Related material on Federated Learning

Federated Learning

Related concepts: Differential privacy, Multi-Party Computation, Collaborative Learning.

So far, the list is ordered randomly, without specific rules, which will be improved in future.

Paper

1. • How To Backdoor Federated Learning link; code
2. • Federated Learning: Strategies for Improving Communication Efficiency link
3. • Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning link
4. • Communication-Efficient Learning of Deep Networks from Decentralized Data from Google 2016. The term Federated Learning was first used in this paper. link

5. A Generic Framework for Privacy Preserving Peep Pearning A detailed explanation of PySyft. link

6. • Federated Machine Learning: Concept and Applications from Qiang Yang, etc. link

7. Federated Learning for Mobile Keyboard Prediction by Google. link

8. Federated Optimization: Distributed Machine Learning for On-Device Intelligence from Google. link

9. Towards Federated Learning at Scale: System Design from Google. link

10. SecureML: A system for Scalable Privacy-Perserving Machine Learning related to Multi-party Computation. link

11. Learning Differentially Private Recurrent Language Models combine differentially private and federated learning. link

12. • Practical Secure Aggregation for Privacy-Preserving Machine Learning, secure aggregation to protect model from inference attack, from Google. link

13. Deep Learning with Differential Privacy from Google. link

14. Privacy-Preserving Deep Learning, introduced synchronized SGD. link

15. Exploiting Unintended Feature Leakage in Collaborative Learning, an attack method related to membership inference, from UCL and Cornell. link; code

16. Membership Inference Attacks Against Machine Learning Models an paper on membership inference attack, from Cornell and etc. link; code

Blog and Tutorial

1. Federated Learning: Collaborative Machine Learning without Centralized Training Data from Google AI Blog. link

2. Private AI — Federated Learning with PySyft and PyTorch from André Macedo Farias. link

3. An Overview of Federated Learning from Basil Han. This blog introduces some challenges of federated learning, including Inference Attack and Model Poisoning.link

4. Federated Learning in 10 lines of PyTorch and PySyft from OpenMined. link

5. An Open Framework for Secure and Privated AI from ODSC. link

6. A Brief Introduction to Differential Privacy from Georgian Partners. link

7. A beginners Guided to Federated Learning from Dr. Santanu Bhattacharya. link.

   Federated Learning was born at the intersection of on-device AI, blockchain, and edge computing/IoT.

8. Federated Learning: The Future of Distributed Machine Learning from Synced. link

9. Important Federated Learning an online comic from Google AI. link

10. Under The Hood of The Pixel 2: How AI Is Supercharging Hardware from Google. link

Tool

1. PySyft Github

2. Tensorflow Federated link; Github

MOOC

1. Secure and Private AI Udacity