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Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism

In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficu...

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Autores principales: Liu, Shun, Zhou, Funa, Tang, Shanjie, Hu, Xiong, Wang, Chaoge, Wang, Tianzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606357/
https://www.ncbi.nlm.nih.gov/pubmed/37895591
http://dx.doi.org/10.3390/e25101470
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author Liu, Shun
Zhou, Funa
Tang, Shanjie
Hu, Xiong
Wang, Chaoge
Wang, Tianzhen
author_facet Liu, Shun
Zhou, Funa
Tang, Shanjie
Hu, Xiong
Wang, Chaoge
Wang, Tianzhen
author_sort Liu, Shun
collection PubMed
description In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model’s performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification.
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spelling pubmed-106063572023-10-28 Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism Liu, Shun Zhou, Funa Tang, Shanjie Hu, Xiong Wang, Chaoge Wang, Tianzhen Entropy (Basel) Article In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model’s performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification. MDPI 2023-10-21 /pmc/articles/PMC10606357/ /pubmed/37895591 http://dx.doi.org/10.3390/e25101470 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
Liu, Shun
Zhou, Funa
Tang, Shanjie
Hu, Xiong
Wang, Chaoge
Wang, Tianzhen
Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title_full Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title_fullStr Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title_full_unstemmed Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title_short Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
title_sort dynamic semi-supervised federated learning fault diagnosis method based on an attention mechanism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606357/
https://www.ncbi.nlm.nih.gov/pubmed/37895591
http://dx.doi.org/10.3390/e25101470
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