The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for distributed learning. To address these issues, this paper presents a novel approach that ensures energy efficiency for distributionally robust federated learning (FL) with over air computation (AirComp). In this context, to effectively balance robustness with energy efficiency, we introduce a novel client selection method that integrates two complementary insights: a deterministic one that is designed for energy efficiency, and a probabilistic one designed for distributional robustness. Simulation results underscore the efficacy of the proposed algorithm, revealing its superior performance compared to baselines from both robustness and energy efficiency perspectives, achieving more than 3-fold energy savings compared to the considered baselines.

翻译：随着无线边缘设备数量的持续增长，能量、带宽、延迟和数据异构性等方面的挑战愈发凸显，这些挑战已成为分布式学习的瓶颈。针对这些问题，本文提出了一种新颖方法，在基于空中计算的分布鲁棒联邦学习中确保能量效率。在此背景下，为有效平衡鲁棒性与能量效率，我们引入了一种新型客户端选择方法，该方法融合了两种互补的见解：一种旨在实现能量效率的确定性策略，以及一种旨在实现分布鲁棒性的概率性策略。仿真结果凸显了所提算法的有效性，表明其在鲁棒性和能量效率两方面均优于基线方法，与所考虑的基线相比实现了超过三倍的能量节省。