Asynchronous Decentralized Learning over Unreliable Wireless Networks

Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers. In this work, we propose an asynchronous decentralized stochastic...

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Bibliographic Details
Main Authors Jeong, Eunjeong, Zecchin, Matteo, Kountouris, Marios
Format Journal Article
LanguageEnglish
Published 02.02.2022
Subjects
Computer Science - Information Theory
Computer Science - Learning
Mathematics - Information Theory
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Summary:Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers. In this work, we propose an asynchronous decentralized stochastic gradient descent (DSGD) algorithm, which is robust to the inherent computation and communication failures occurring at the wireless network edge. We theoretically analyze its performance and establish a non-asymptotic convergence guarantee. Experimental results corroborate our analysis, demonstrating the benefits of asynchronicity and outdated gradient information reuse in decentralized learning over unreliable wireless networks.
DOI:10.48550/arxiv.2202.00955