SSFL-DCSL combines Laplace-weighted pseudo-labels, local and global contrastive losses, and momentum-updated prototypes to train personalized fault diagnosis models across clients with few labels.
Uncertainty-aware deep learning: A promising tool for trustworthy fault diagnosis,
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Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
SSFL-DCSL combines Laplace-weighted pseudo-labels, local and global contrastive losses, and momentum-updated prototypes to train personalized fault diagnosis models across clients with few labels.