Over-the-Air Fair Federated Learning via Multi-Objective Optimization

• URL: http://arxiv.org/abs/2501.03392v1
• Date: Mon, 06 Jan 2025 21:16:51 GMT
• Title: Over-the-Air Fair Federated Learning via Multi-Objective Optimization
• Authors: Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad, H. Vincent Poor,
• Abstract summary: We propose an over-the-air fair federated learning algorithm (OTA-FFL) to train fair FL models. Experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance.
• Score: 52.295563400314094
• License:
• Abstract: In federated learning (FL), heterogeneity among the local dataset distributions of clients can result in unsatisfactory performance for some, leading to an unfair model. To address this challenge, we propose an over-the-air fair federated learning algorithm (OTA-FFL), which leverages over-the-air computation to train fair FL models. By formulating FL as a multi-objective minimization problem, we introduce a modified Chebyshev approach to compute adaptive weighting coefficients for gradient aggregation in each communication round. To enable efficient aggregation over the multiple access channel, we derive analytical solutions for the optimal transmit scalars at the clients and the de-noising scalar at the parameter server. Extensive experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance compared to existing methods.

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