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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Bibliographic Details
Published in:	IEEE transactions on wireless communications Vol. 19; no. 11; pp. 7108 - 7123
Main Authors:	Xia, Wenchao, Quek, Tony Q. S., Guo, Kun, Wen, Wanli, Yang, Howard H., Zhu, Hongbo
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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