Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism
Author:
Liu Shun1, Zhou Funa1, Tang Shanjie1, Hu Xiong1, Wang Chaoge1, Wang Tianzhen1ORCID
Affiliation:
1. School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China
Abstract
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.
Funder
National Natural Science Foundation of China National Natural Science Foundation Youth Science Foundation Project
Subject
General Physics and Astronomy
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