Balanced Energy Consumption Based on Historical Participation of
Resource-Constrained Devices in Federated Edge Learning
To appear in IEEE
In recent years, Federated Edge Learning has gained interest from both
industry and academia for deployment at the wireless network edge.
However, some resource-restricted edge devices (EDs) bear more
computation and communication loads due to the heterogeneity of data and
resources. Several approaches have been proposed in the literature to
reduce energy costs by scheduling only a few EDs to complete training
tasks based on their energy budgets. Nevertheless, from a practical
perspective, the incongruent data distribution cannot be captured,
resulting in a biased model for EDs that are frequently selected.
Furthermore, the frequently scheduled devices deplete their energy
quickly, making them inaccessible. Thus, this paper proposes a novel
scheduling policy based on the historical participation of each ED that
ensures an unbiased model while balancing learning tasks so that all EDs
consume equivalent energy at the end of the training. We formulate an
optimization problem based on Jain’s fairness index, followed by
tractable algorithms to solve this problem. Extensive experiments have
been conducted, and the results show that the proposed algorithm
balances the energy consumption among EDs and accelerates the
convergence rate while achieving satisfactory performance.