This paper introduces a novel approach for energy-efficient and distributionally robust federated learning (FL) using over-the-air computation (AirComp). It addresses challenges posed by energy consumption, bandwidth limitations, latency, and data heterogeneity in distributed learning. A novel client selection method is proposed, integrating deterministic (energy-efficient) and probabilistic (distributionally robust) insights. Simulation results demonstrate superior performance compared to baselines, achieving over three-fold energy savings.
Publisher
Published On
Authors
Mohamed Badi, Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis
Tags
federated learning
energy efficiency
over-the-air computation
client selection
data heterogeneity
robustness
simulation results
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