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|a 10.1109/TPAMI.2023.3243080
|2 doi
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|a eng
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| 100 |
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|a Zhou, Shenglong
|e verfasserin
|4 aut
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| 245 |
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|a Federated Learning Via Inexact ADMM
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 03.07.2023
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|a Date Revised 03.07.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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 develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has high numerical performance compared with several state-of-the-art algorithms for federated learning
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|a Journal Article
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|a Li, Geoffrey Ye
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 8 vom: 07. Aug., Seite 9699-9708
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|x 1939-3539
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|g volume:45
|g year:2023
|g number:8
|g day:07
|g month:08
|g pages:9699-9708
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|u http://dx.doi.org/10.1109/TPAMI.2023.3243080
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