A curated list of FL system-related academic papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field.

Papers with 🎓 have been peer-reviewed and presented in academic conferences.

Cross-device

• Apple: Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications | PDF, PDF
• Google: Towards Federated Learning at Scale: System Design | PDF, Github🎓
• Microsoft: FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations | PDF, Github
• Meta: | Papaya: Practical, Private, and Scalable Federated Learning PDF 🎓

Cross-silo

• IBM: | IBM Federated Learning: An Enterprise Framework White Paper | PDF
• Nvidia: | Federated Learning for Healthcare Using NVIDIA Clara | PDF
• WeBank: Federated Learning White Paper V1.0 | PDF, Github

• Cisco: Flame | Github
• OpenMined: PySyft | Github
• Baidu: Paddle | Github
• ByteDance: Fedlearner | Github

• OpenMined: PyVertical Github, PDF

• FedScale: Benchmarking Model and System Performance of Federated Learning 🎓
• EasyFL: A Low-code Federated Learning Platform For Dummies
• Flower: A Friendly Federated Learning Research Framework
• Sherpa: Federated Learning and Differential Privacy Framework: Protect user privacy without renouncing the power of Artificial Intelligence
• FedML: A Research Library and Benchmark for Federated 🎓Machine Learning
• LEAF: A Benchmark for Federated Settings 🎓
• FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning

• System Optimization in Synchronous Federated Training: A Survey
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• A Field Guide to Federated Optimization
• Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
• Advances and Open Problems in Federated Learning

• Oort: Efficient Federated Learning via Guided Participant Selection | OSDI 21 🎓
• Mistify: Automating DNN Model Porting for On-Device Inference at the Edge | NSDI 21 🎓

• https://github.com/innovation-cat/Awesome-Federated-Machine-Learning
• https://github.com/chaoyanghe/Awesome-Federated-Learning
• https://github.com/weimingwill/awesome-federated-learning#resource-allocation