Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things

Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an F...

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Vydané v:	IEEE internet of things journal Ročník 9; číslo 6; s. 4385 - 4395
Hlavní autori:	Yu, Liangkun, Albelaihi, Rana, Sun, Xiang, Ansari, Nirwan, Devetsikiotis, Michael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 15.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Client selection Clients Computational modeling Data models Energy consumption Federated learning federated learning (FL) Internet of Things Iterative methods Machine learning Model accuracy Network latency Optimization Resource management Servers Training waiting time Wireless communication
ISSN:	2327-4662, 2327-4662
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