Federated Learning Via Inexact ADMM

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this article, we deve...

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Bibliographic Details

Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 8; pp. 9699 - 9708
Main Authors:	Zhou, Shenglong, Li, Geoffrey Ye
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation algorithms Communication and computation-efficiency Convergence Convex functions Federated learning global convergence inexact ADMM Learning Machine learning Optimization partial device participation Servers Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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