Towards Federated Learning at Scale: System Design
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, Daniel Ramage, Jason Roselander
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
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.
Linked by 1 identifiable commenter in 1 comment across 1 thread, from Feb 2019 to Feb 2019. Found in 2 HN submissions from Feb 2019 to May 2019. Open submission discussions.
100! This recent paper from Google combines FL with secure multi-party computation for “secure aggregation” to prevent the aggregate server from even seeing the gradients https://export.arxiv.org/abs/1902.01046
100! This recent paper from Google combines FL with secure multi-party computation for “secure aggregation” to prevent the aggregate server from even seeing the gradients https://export.arxiv.org/abs/1902.01046
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