Towards Federated Learning at Scale: System Design
This work provides a scalable solution for federated learning on mobile devices, which is incremental as it builds on existing methods to handle real-world deployment challenges.
The paper tackles the challenge of scaling federated learning by designing a production system for mobile devices using TensorFlow, resulting in a practical implementation that addresses key system-level issues.
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.