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-le...

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Hauptverfasser:	Bonawitz, Keith, Eichner, Hubert, Grieskamp, Wolfgang, Huba, Dzmitry, Ingerman, Alex, Ivanov, Vladimir, Kiddon, Chloe, Konečný, Jakub, Mazzocchi, Stefano, McMahan, H. Brendan, Van Overveldt, Timon, Petrou, David, Ramage, Daniel, Roselander, Jason
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning Statistics - Machine Learning
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Beschreibung
Zusammenfassung:	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.
DOI:	10.48550/arxiv.1902.01046