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.

本研究解决了联邦学习中客户端局部数据集分布不均导致模型不公平的问题。我们提出了一种通过无线计算训练公平模型的联邦学习算法OTA-FFL，利用多目标最小化方法并引入改进的切比雪夫方法以计算自适应权重。实验结果显示，OTA-FFL在公平性和鲁棒性能方面优于现有方法。

通过多目标优化实现的无线公平联邦学习