Towards Federated Learning at Scale: System Design

作者：Keith Bonawitz, Hubert Eichner, W. Grieskamp, Dzmitry Huba, A. Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, S. Mazzocchi, H. B. McMahan, Timon Van Overveldt, D. Petrou, Daniel Ramage, Jason Roselander · 发表于：USENIX workshop on Tackling computer systems problems with machine learning techniques · 年份：2019 · 被引用次数：3296 · 研究领域：Computer Science、Mathematics

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.