Federated Learning Over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL imp...

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Vydané v:	IEEE journal on selected areas in communications Ročník 39; číslo 12; s. 3723 - 3741
Hlavní autori:	Xing, Hong, Simeone, Osvaldo, Bi, Suzhi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Channel noise Clients Cloud computing Collaborative work Convergence Convexity D2D networks Data centers Data models decentralized stochastic gradient descent Device-to-device communication distributed learning Federated learning Image classification Internet of Things Machine learning over-the-air computation random linear coding Signal to noise ratio Stochastic processes Topology Wireless communications Wireless networks
ISSN:	0733-8716, 1558-0008
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