Work

Towards Federated Learning at Scale: System Design

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1902.01046

Authors: 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

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2913570153

Cites: 21

Cited by: 2,624

Related to: 10