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Towards Federated Learning at Scale: System Design
February 04, 2019 Β· Declared Dead Β· π USENIX workshop on Tackling computer systems problems with machine learning techniques
"No code URL or promise found in abstract"
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Authors
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub KoneΔnΓ½, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
arXiv ID
1902.01046
Category
cs.LG: Machine Learning
Cross-listed
cs.DC,
stat.ML
Citations
3.0K
Venue
USENIX workshop on Tackling computer systems problems with machine learning techniques
Last Checked
1 month ago
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.
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