<ul><li>This study focuses on the energy implications of federated learning (FL) within Artificial Intelligence of Things (AIoT) scenarios.</li><li>The research examines the energy consumed during the FL process, highlighting pre-processing, communication, and local learning as the main energy-intensive processes.</li><li>The study proposes two clustering-informed methods for device/client selection in distributed AIoT settings, aiming to speed up model training convergence.</li><li>Through extensive numerical experimentation, the clustering strategies show high convergence rates and low energy consumption compared to other recent approaches in the literature.</li></ul>

Energy-Efficient Federated Learning for AIoT using Clustering Methods

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