ICC 2022, IEEE 1st International Workshop on Semantic Communications, 16-20 May 2022, Seoul, South Korea (Virtual Event)
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the benefits of asynchronicity and outdated gradient information reuse in decentralized learning over unreliable wireless networks.
Type:
Conférence
City:
Seoul
Date:
2022-05-16
Department:
Systèmes de Communication
Eurecom Ref:
6800
Copyright:
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