Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation str...

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Bibliographic Details
Main Authors: Funa Zhou, Yi Yang, Chaoge Wang, Xiong Hu
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Entropy
Subjects:
fault diagnosis
federated learning
imbalance mode mismatch
intra-client imbalance degree
joint optimization
Online Access:https://www.mdpi.com/1099-4300/25/4/606

Internet

https://www.mdpi.com/1099-4300/25/4/606

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