Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA

• URL: http://arxiv.org/abs/2309.06033v1
• Date: Tue, 12 Sep 2023 08:05:39 GMT
• Title: Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA
• Authors: Rafael Valente da Silva, Onel L. Alcaraz L\'opez, and Richard Demo Souza
• Abstract summary: Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL) This letter considers the integration of energy harvesting (EH) devices into a FL network with multi-channel ALOHA. Numerical results demonstrate the effectiveness of this method, particularly in critical setups.
• Score: 3.7769304982979666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited battery and heterogeneous energy availability, while multiple rounds are required in FL for convergence, intensifying the need for energy efficiency. Energy depletion may hinder the training process and the efficient utilization of the trained model. To solve these problems, this letter considers the integration of energy harvesting (EH) devices into a FL network with multi-channel ALOHA, while proposing a method to ensure both low energy outage probability and successful execution of future tasks. Numerical results demonstrate the effectiveness of this method, particularly in critical setups where the average energy income fails to cover the iteration cost. The method outperforms a norm based solution in terms of convergence time and battery level.

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