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

Yifan Luo, Jindan Xu, Wei Xu*, Kezhi Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is underexplored. 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.
Original languageEnglish
Pages (from-to)503-507
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number2
Early online date20 Oct 2020
DOIs
Publication statusPublished - 1 Feb 2021

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