Class-imbalance privacy-preserving federated learning for decentralized fault diagnosis with biometric authentication
Privacy protection as a major concern of the industrial big data enabling entities makes the
massive safety-critical operation data of a wind turbine unable to exert its great value …
massive safety-critical operation data of a wind turbine unable to exert its great value …
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions
Federated learning has been receiving increasing attention in the recent years, which
improves model performance with data privacy among different clients. The intelligent fault …
improves model performance with data privacy among different clients. The intelligent fault …
Federated domain generalization: A secure and robust framework for intelligent fault diagnosis
The maturation of sensor network technologies has promoted the emergence of the
Industrial Internet of Things, which has been collecting an increasing volume of monitoring …
Industrial Internet of Things, which has been collecting an increasing volume of monitoring …
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …
popularly developed in the past years. While promising diagnostic performance has been …
Federated transfer learning in fault diagnosis under data privacy with target self-adaptation
X Li, C Zhang, X Li, W Zhang - Journal of Manufacturing Systems, 2023 - Elsevier
The past decades have witnessed great developments and applications of the data-driven
machinery fault diagnosis methods. Due to the difficulties and significant expenses in …
machinery fault diagnosis methods. Due to the difficulties and significant expenses in …
Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging
Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …
methods relies on abundant labeled fault samples under various working conditions, while …
Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client
F Zhou, S Liu, H Fujita, X Hu, Y Zhang, B Wang… - Expert Systems with …, 2024 - Elsevier
Federated Learning is a promising tool for fault diagnosis of critical components for electrical
driving systems. However, the performance of existing method is limited by negative …
driving systems. However, the performance of existing method is limited by negative …
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy
Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …
years, and promising diagnostic performance has been achieved with sufficient monitoring …
BCE-FL: a secure and privacy-preserving federated learning system for device fault diagnosis under non-IID condition in IIoT
Traditional device fault diagnostic methods in Industrial Internet of Things (IIoT) require
nodes to upload local data to the cloud, which, however, may lead to privacy leakage issues …
nodes to upload local data to the cloud, which, however, may lead to privacy leakage issues …
Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy
Intelligent data-driven machinery fault diagnosis methods have been popularly developed in
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
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