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.
@article{arxiv.1902.01046,
title = {Towards Federated Learning at Scale: System Design},
author = {Keith Bonawitz and Hubert Eichner and Wolfgang Grieskamp and Dzmitry Huba and Alex Ingerman and Vladimir Ivanov and Chloe Kiddon and Jakub Konečný and Stefano Mazzocchi and H. Brendan McMahan and Timon Van Overveldt and David Petrou and Daniel Ramage and Jason Roselander},
journal= {arXiv preprint arXiv:1902.01046},
year = {2019}
}