Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning
This addresses energy and robustness bottlenecks for wireless edge devices in distributed learning, representing an incremental improvement.
The paper tackled the challenge of balancing energy efficiency and distributional robustness in over-the-air federated learning by introducing a novel client selection method, achieving more than 3-fold energy savings compared to baselines.
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.