Personalizing Federated Learning with Over-the-Air Computations

• URL: http://arxiv.org/abs/2302.12509v1
• Date: Fri, 24 Feb 2023 08:41:19 GMT
• Title: Personalizing Federated Learning with Over-the-Air Computations
• Authors: Zihan Chen, Zeshen Li, Howard H. Yang, Tony Q.S. Quek
• Abstract summary: Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
• Score: 84.8089761800994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often throttled by challenges arising from limited communication and data heterogeneity. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue. As a result, it enhances the generalization and robustness of each client's local model. We elaborate on the model training procedure and its advantages over conventional frameworks. We provide a convergence analysis that theoretically demonstrates the training efficiency. We also conduct extensive experiments to validate the efficacy of the proposed framework.

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