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Semantic Scholar Open Access 2019 3096 sitasi

Towards Federated Learning at Scale: System Design

Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman +9 lainnya
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Abstrak

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.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (14)

K

Keith Bonawitz

H

Hubert Eichner

W

W. Grieskamp

D

Dzmitry Huba

A

A. Ingerman

V

Vladimir Ivanov

C

Chloé Kiddon

J

Jakub Konecný

S

S. Mazzocchi

H

H. B. McMahan

T

Timon Van Overveldt

D

David Petrou

D

Daniel Ramage

J

Jason Roselander

Format Sitasi

APA MLA BibTeX
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V. et al. (2019). Towards Federated Learning at Scale: System Design. https://www.semanticscholar.org/paper/79cf9462a583e1889781868cbf8c31e43b36dd2f

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Tahun Terbit
2019
Bahasa
en
Total Sitasi
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