Energy-Aware Federated Learning With Distributed User Sampling and Multichannel ALOHA
Valente Da Silva, Rafael; Lopez, Onel L. Alcaraz; Souza, Richard Demo (2023-09-07)
Valente Da Silva, Rafael
Lopez, Onel L. Alcaraz
Souza, Richard Demo
IEEE
07.09.2023
R. Valente da Silva, O. L. A. López and R. D. Souza, "Energy-Aware Federated Learning With Distributed User Sampling and Multichannel ALOHA," in IEEE Communications Letters, vol. 27, no. 10, pp. 2867-2871, Oct. 2023, doi: 10.1109/LCOMM.2023.3312793
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202311273362
https://urn.fi/URN:NBN:fi:oulu-202311273362
Tiivistelmä
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.
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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