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A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate

Published Online: 2023-12-14

Published Print: 2023-12

Authors

Zhou, Funa https://orcid.org/0000-0003-3592-9664
Xu, Wei

Wang, Chaoge

Hu, Xiong

Wang, Tianzhen
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (62073213, 52205111)
License Information

Text and Data Mining valid from 2023-12-01

Version of Record valid from 2023-12-01
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Article History

Received: 7 September 2023

Accepted: 27 November 2023

First Online: 14 December 2023

Declarations

:

: Not applicable.

: Not applicable.

: Not applicable.

: The authors have no relevant financial or non-financial interests to disclose.

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