Multi-Armed Bandit-Based Client Scheduling for Federated Learning

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and uplo...

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Vydané v:	IEEE transactions on wireless communications Ročník 19; číslo 11; s. 7108 - 7123
Hlavní autori:	Xia, Wenchao, Quek, Tony Q. S., Guo, Kun, Wen, Wanli, Yang, Howard H., Zhu, Hongbo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms client scheduling client selection Clients Communication Computational modeling Confidence Convergence Data models Datasets Federated learning multi-armed bandit (MAB) Multi-armed bandit problems Scheduling Smart phones Training Upper bound Upper bounds Wireless communication
ISSN:	1536-1276, 1558-2248
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