Energy-aware Edge Federated Learning for Enhanced Reliability and Sustainability

Abstract

Federated Learning (FL) has emerged as a value added proposition for use in edge-based infrastructures, distributing the training process among collaborative workers without disclosing raw (user) data. In this context, we argue that, differently from what already present in most current literature, energy consumption of nodes (either workers or the ensembler node) is a central element to consider in FL, e.g., to have a more sustainable FL node selection strategy. To this end, a complete and detailed report about energy consumption at each FL round is required to allow for innovative and greener resource management approaches, taking into account residual energy and learning completion time of participating FL nodes. Filling this gap, we present the design of a novel distributed framework capable of collecting accurate (worker) energy expenditure and learning-centric metrics at each FL round. The frame-work comprises state-of-the-art technological building blocks, purposely integrated to enable advanced and energy-aware FL process orchestration capabilities. To validate the approach, we rely on a heterogeneous experimental testbed, and conduct a distributed learning process employing a realistic dataset. The preliminary evaluation results reported in this paper highlight the potential advantage in terms of overall energy consumption reduction and the suitability of an adaptive learning framework capable of autonomous evaluations of the most proper trade-off to apply between accuracy and energy expenditure.