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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal on selected areas in communications Ročník 39; číslo 12; s. 3723 - 3741
Hlavní autoři:	Xing, Hong, Simeone, Osvaldo, Bi, Suzhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!