Mobility-Aware Federated Learning Considering Multiple Networks

Author:

Macedo Daniel1ORCID,Santos Danilo2ORCID,Perkusich Angelo2ORCID,Valadares Dalton C. G.12ORCID

Affiliation:

1. Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, Paraiba, Brazil

2. Virtus RDI Center, Federal University of Campina Grande, Campina Grande 58429-900, Paraiba, Brazil

Abstract

Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects.

Funder

Virtus Research, Development and Innovation Center

Programa de Pós-Graduação em Engenharia Elétrica

Federal University of Campina Grande

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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