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

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. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, 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
$\mathcal {O} (\frac{1}{T})$
. 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.