Towards Federated Learning at Scale

In plain words: Phones train a shared model together using their own data, keeping personal data on the device instead of uploading it. The team built a working production system for this and describes the design choices and problems they solved.

Abstract · Towards Federated Learning at Scale: System Design

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.

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, et al.
arXiv:1902.01046 · cs.LG, cs.DC, stat.ML · submitted Feb 4, 2019 · updated Mar 22, 2019
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