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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2024

02.08.2023 | Original Article

Federated domain generalization for intelligent fault diagnosis based on pseudo-siamese network and robust global model aggregation

verfasst von: Yan Song, Peng Liu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2024

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Abstract

Federated learning (FL) based intelligent fault diagnosis has developed rapidly in recent years owing to the need for data privacy. However, models trained using FL may suffer from performance degradation when applied to unseen domains. In this regard, we propose a federated domain generalization approach using a pseudo-Siamese network (PSN) and robust model aggregation for intelligent fault diagnosis. Firstly, the proposed method employs PSN to calculate the discrepancy between client and global models at the local clients. This enhances the feature space boundary of fault diagnosis models. Then the proposed method computes cross-classification losses of locally trained global models on the central server for robust model aggregation. Finally, we evaluate our approach through experiments where local clients contain data from varying datasets. Experimental results on the proposed method and other transfer learning and federated learning methods prove the outperformance of the proposed method.
Vorheriger Artikel Adaptive occlusion hybrid second-order attention network for head pose estimation
Nächster Artikel Adaptive weighting function for weighted nuclear norm based matrix/tensor completion

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Metadaten
Titel
Federated domain generalization for intelligent fault diagnosis based on pseudo-siamese network and robust global model aggregation
verfasst von
Yan Song
Peng Liu
Publikationsdatum
02.08.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01934-2

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