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

Yan, Na, Wang, Kezhi, Pan, Cunhua and Chai, Kok Keong (2022) Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks. IEEE Signal Processing Letters, 29. pp. 772-776. ISSN 1070-9908

Performance_Analysis_for_Channel-Weighted_Federated_Learning_in_OMA_Wireless_Networks.pdf - Accepted Version

Download (1MB) | Preview
Official URL:


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.

Item Type: Article
Additional Information: Funding information: This work of Na Yan was supported by China Scholarship Council.
Uncontrolled Keywords: Federated learning, aggregation of gradients, orthogonal multiple access, convergence analysis
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 21 Mar 2022 11:04
Last Modified: 08 Apr 2022 13:45

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics