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.

通过融合设计用于节能和分布鲁棒性的确定性和概率方法，本文提出了一种保证分布式鲁棒联邦学习的能量效率的新方法，并且通过模拟结果展示了与基准方法相比，在鲁棒性和能量效率两个方面都具有优异性能，达到了超过基准方法3倍的能量节省。

在无线联邦学习中平衡能效和分布鲁棒性