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    Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism by Shun Liu, Funa Zhou, Shanjie Tang, Xiong Hu, Chaoge Wang, Tianzhen Wang

    Published 2023-10-01
    “…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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