Personalized Federated Learning Over the Air

Zeshen Li, Zihan Chen, Tony Q. S. Quek, Howard H. Yang

Abstract: We propose an effective approach toward implementing personalized federated learning at the edge of wireless networks. The scheme employs a bi-level optimization framework to personalize the federated learning models, and leverages over-the-air computations for model aggregation. We identify a mutual benefit in such a design. Specifically, personalized federated learning models address the challenge of data heterogeneity in federated learning, while over-the-air computations, which capitalize on the superposition property of multiple access channels, enable all clients to upload their intermediate parameters in each communication round for global aggregation, significantly enhancing system scalability. However, the channel fading and heavy-tailed noise introduced by over-the-air computations pose challenges to the robustness of personalized federated learning models. By adopting a bi-level optimization framework, we improve the stability of personalized federated learning models based on over-the-air computations, establishing a scalable and robust federated edge learning system. We also derive convergence rates of the proposed algorithm, encompassing key factors such as model compression, channel fading, and heavy-tailed noise. The analysis offers a comprehensive understanding of how system configurations affect training performance. We corroborate the efficacy of our framework via extensive experiments.

External IDs:doi:10.1109/twc.2025.3573777