通过空中计算实现更公平的联邦学习，提升弱客户端性能。
Over-the-Air Fair Federated Learning via Multi-Objective Optimization
- 将联邦学习建模为多目标优化问题，自适应调整梯度聚合权重。
- 在多个客户端场景下，显著提升最差客户端的模型准确率。
- 适合对公平性要求高的分布式学习场景，如医疗数据协作。
在联邦学习中，客户端间本地数据分布的异质性可能导致部分客户端表现不佳，造成模型不公平。为此，本文提出一种基于空中计算的公平联邦学习算法（OTA-FFL），通过将联邦学习建模为多目标最小化问题，引入改进的切比雪夫方法，在每轮通信中动态计算梯度聚合的自适应权重系数。为实现多接入信道上的高效聚合，推导出客户端最优发射缩放因子和参数服务器去噪缩放因子的解析解。大量实验表明，与现有方法相比，OTA-FFL 在提升公平性和鲁棒性方面具有显著优势。
原文摘要 · Abstract (English)
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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