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Mobility-Aware Federated Learning Considering Multiple Networks
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 ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386473/ https://www.ncbi.nlm.nih.gov/pubmed/37514581 http://dx.doi.org/10.3390/s23146286 |
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author | Macedo, Daniel Santos, Danilo Perkusich, Angelo Valadares, Dalton C. G. |
author_facet | Macedo, Daniel Santos, Danilo Perkusich, Angelo Valadares, Dalton C. G. |
author_sort | Macedo, Daniel |
collection | PubMed |
description | 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 [Formula: see text] more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects. |
format | Online Article Text |
id | pubmed-10386473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103864732023-07-30 Mobility-Aware Federated Learning Considering Multiple Networks Macedo, Daniel Santos, Danilo Perkusich, Angelo Valadares, Dalton C. G. Sensors (Basel) Article 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 [Formula: see text] more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects. MDPI 2023-07-10 /pmc/articles/PMC10386473/ /pubmed/37514581 http://dx.doi.org/10.3390/s23146286 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Macedo, Daniel Santos, Danilo Perkusich, Angelo Valadares, Dalton C. G. Mobility-Aware Federated Learning Considering Multiple Networks |
title | Mobility-Aware Federated Learning Considering Multiple Networks |
title_full | Mobility-Aware Federated Learning Considering Multiple Networks |
title_fullStr | Mobility-Aware Federated Learning Considering Multiple Networks |
title_full_unstemmed | Mobility-Aware Federated Learning Considering Multiple Networks |
title_short | Mobility-Aware Federated Learning Considering Multiple Networks |
title_sort | mobility-aware federated learning considering multiple networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386473/ https://www.ncbi.nlm.nih.gov/pubmed/37514581 http://dx.doi.org/10.3390/s23146286 |
work_keys_str_mv | AT macedodaniel mobilityawarefederatedlearningconsideringmultiplenetworks AT santosdanilo mobilityawarefederatedlearningconsideringmultiplenetworks AT perkusichangelo mobilityawarefederatedlearningconsideringmultiplenetworks AT valadaresdaltoncg mobilityawarefederatedlearningconsideringmultiplenetworks |