Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks

TL;DR

This paper introduces a sliding differential evolution scheduling policy for federated learning in bandwidth-limited networks, effectively reducing energy consumption and accelerating model convergence.

Contribution

It proposes a novel SDES policy that optimizes energy use and convergence speed in federated learning with limited bandwidth and energy resources.

Findings

SDES reduces energy consumption compared to existing policies.

SDES accelerates model convergence in bandwidth-limited settings.

SDES has lower computational complexity than current methods.

Abstract

Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored. In this paper, to jointly save energy consumed by the battery-limited UEs and accelerate the convergence of the global model in FL for the bandwidth-limited network, we propose the sliding differential evolution-based scheduling (SDES) policy. To this end, we first formulate an optimization that aims to minimize a weighted sum of energy consumption and model training convergence. Then, we apply the SDES with parallel differential evolution (DE) operations in several small-scale windows, to address the above proposed problem effectively. Compared with existing scheduling policies, the proposed SDES performs well in reducing energy consumption and the model convergence with lower computational complexity.