Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.
REF further presented a high-level design for scalable FL production system in the setting of mobile devices and discussed some challenges, solutions, and open problems in the system design.
Towards Federated Learning at Scale: System Design
ArXiv
ArXiv
Computer Science Mathematics