Chapter 06
Federated Learning System
Federated Learning System
Papers
-
Towards Federated Learning at Scale: System Design(https://arxiv.org/abs/1902.01046) [MLSys'19]
-
BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning(https://www.usenix.org/system/files/atc20-zhang-chengliang.pdf)(https://github.com/marcoszh/BatchCrypt) [ATC'20]
Projects
- FATE @ Webank(https://github.com/FederatedAI/FATE)
- Tensorflow Federated @ Google(https://github.com/tensorflow/federated)
- PySyft @ OpenMined(https://github.com/OpenMined/PySyft)
- A Generic Framework for Privacy Preserving Peep Pearning(https://arxiv.org/abs/1811.04017)
- PaddleFL @ Baidu(https://github.com/PaddlePaddle/PaddleFL)
- Nvidia Clara SDK(https://developer.nvidia.com/clara)
- Flower(https://github.com/adap/flower)(https://flower.dev/)(https://arxiv.org/abs/2007.14390)
- A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.