A curated list of research in federated learning. Link to the code if available is also present. You are very welcome to pull request by using our template.
Federated learning research is booming. We organize the papers by their targeting problem and by conference.Last update: 04 Dec, 2020
- Statistical Heterogeneity
- Communication Efficiency
- System: federated learning system design, frameworks, edge AI, etc.
- Trustworthiness: privacy, security, fairness
- Decentralzied FL
- Applications
- Vertical FL
- FL + {X}: FL + reinforcement learning, FL + transfer learning, etc.
- Communication-Efficient Learning of Deep Networks from Decentralized Data [Paper] [Github] [Google] [Must Read]
- Federated learning paper by conferences: NIPS, ICML, ICLR, etc.
- Federated learning paper by journal
- Federated Learning Comic [Google Blog]
- Federated Learning: Collaborative Machine Learning without Centralized Training Data [Google Blog]
- Federated Machine Learning: Concept and Applications [Paper]
- Federated Learning: Challenges, Methods, and Future Directions [Paper]
- Advances and Open Problems in Federated Learning [Paper]
- Federated Learning White Paper V1.0 [Paper]
- Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection [Paper]
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey [Paper]
- Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges [Paper]
- A Review of Applications in Federated Learning [Paper]
- LEAF: A Benchmark for Federated Settings [Paper] [Github] [Recommend]
- The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems [Paper]
- Performance Optimization for Federated Person Re-identification via Benchmark Analysis [Paper] [ACMMM20] [Github]
- A Performance Evaluation of Federated Learning Algorithms [Paper]
- Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking [Paper]
- GDPR, Data Shotrage and AI (AAAI-19) [Video]
- Federated Learning: Machine Learning on Decentralized Data (Google I/O'19) [Youtube]
- EasyFL [Paper]
- PySyft [Github]
- A Generic Framework for Privacy Preserving Peep Pearning [Paper]
- Tensorflow Federated [Web]
- FATE [Github]
- FedLearner [Github] ByteDance
- Baidu PaddleFL [Github]
- Nvidia Clara SDK [Web]
- Flower.dev
- OpenFL
- Adap [Website]: Fleet Intelligence
- Privacy.ai [Website]
- OpenMined [Website]
- Arkhn [Website]: Healthcare data
- Owkin [Website]: Medical research
- Snips [Website]: Voice platform
- XAIN [Website] [Github]: Automated Invoicing
- S20 [Website]: Multiple third party collaboration
- DataFleets [Website]
- Decentralized Machine Learning [Website]