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

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 effici...

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Bibliographic Details
Published in:IEEE communications letters 2023-10, Vol.27 (10), p.2867-2871
Main Authors: Valente da Silva, Rafael, Lopez, Onel L. Alcaraz, Souza, Richard Demo
Format: Article
Language:English
Subjects:
Batteries
Computational modeling
Convergence
Energy efficiency
Energy harvesting
Energy management
Federated learning
Internet of Things
Iterative methods
multichannel ALOHA
Task analysis
Training
Transceivers
user sampling
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3312793