Energy-Aware Federated Learning with Distributed User Sampling and
  Multichannel ALOHA
Rafael Valente da Silva Onel L. Alcaraz López Richard Demo Souza
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.