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arxiv: 1902.01046 · v2 · pith:5FLRFMSYnew · submitted 2019-02-04 · 💻 cs.LG · cs.DC· stat.ML

Towards Federated Learning at Scale: System Design

Keith Bonawitz , Hubert Eichner , Wolfgang Grieskamp , Dzmitry Huba , Alex Ingerman , Vladimir Ivanov , Chloe Kiddon , Jakub Kone\v{c}n\'y
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Stefano Mazzocchi H. Brendan McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander
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classification 💻 cs.LG cs.DCstat.ML
keywords learningfederateddesignsystemapproachbuiltchallengescorpus
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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.

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