Federated Learning Over Wireless Networks: Optimizing Performance With NOMA and Power Allocation

This paper addresses the challenge of training federated learning (FL) algorithms over practical wireless networks enhanced by non-orthogonal multiple access (NOMA). In FL procedures, model parameters are transmitted over wireless links, and factors such as packet errors can significantly impact training quality, especially with non-independent and identically distributed (non-IID) datasets. To address this issue, we formulate the complex learning and wireless resource allocation problem as an optimization task aimed at minimizing an FL loss function, thereby capturing the performance of the FL algorithm. Under mild assumptions, we derive the expected convergence rate of the FL algorithm, quantifying the impact of wireless factors on FL. Leveraging this convergence analysis, we determine the optimal transmit power for each user through quadratic transform (QT) and a proposed low-complexity iterative method with successive convex approximation (SCA) serving as benchmark. Extensive simulation results demonstrate that the proposed scheme significantly outperforms traditional schemes from both the optimization and FL performance perspectives.