Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency. In this paper, we propose an Over-the-Air federated learning algorithm that aims to provide fairness and robustness through minmax optimization. By using the epigraph form of the problem at hand, we show that the proposed algorithm converges to the optimal solution of the minmax problem. Moreover, the proposed approach does not require reconstructing channel coefficients by complex encoding-decoding schemes as opposed to state-of-the-art approaches. This improves both efficiency and privacy.

翻译：空中计算是一种超越5G的通信策略，因其高效性近期被证明可用于机器学习模型的去中心化训练。本文提出一种基于空中计算的联邦学习算法，旨在通过极小极大优化实现公平性与鲁棒性。利用所涉及问题的上镜图形式，我们证明该算法能够收敛至极小极大问题的最优解。此外，与现有方法不同，本方法无需通过复杂的编解码方案重建信道系数，从而提升了效率与隐私保护性能。