Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks

Na Yan, Kezhi Wang*, Cunhua Pan*, Kok Keong Chai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices.} \textcolor{blue}{In the proposed scheme}, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel\textcolor{blue}{, which can avoid the synchronization issue among devices faced by over-the-air FL.} The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of O(1/T). Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.

Original languageEnglish
Pages (from-to)772-776
Number of pages5
JournalIEEE Signal Processing Letters
Volume29
Early online date25 Feb 2022
DOIs
Publication statusPublished - 22 Mar 2022

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