Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery
Y Fu, H Cao, X Chen, J Ding - Knowledge-Based Systems, 2022 - Elsevier
Broad learning system (BLS) is widely used in intelligent fault diagnosis (IFD) since its high
computation efficiency and incremental learning ability. However, its applicability is limited to …
computation efficiency and incremental learning ability. However, its applicability is limited to …
Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis
Data‐driven fault diagnosis approaches have been widely adopted due to their persuasive
performance. However, data are always insufficient to develop effective fault diagnosis …
performance. However, data are always insufficient to develop effective fault diagnosis …
WDA: an improved Wasserstein distance-based transfer learning fault diagnosis method
With the growth of computing power, deep learning methods have recently been widely
used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy …
used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy …
[HTML][HTML] Review on application progress of federated learning model and security hazard protection
Federated learning is a new type of distributed learning framework that allows multiple
participants to share training results without revealing their data privacy. As data privacy …
participants to share training results without revealing their data privacy. As data privacy …
Active federated transfer algorithm based on broad learning for fault diagnosis
Federated learning (FL) guaranteeing data privacy is of great interest in decentralized fault
diagnosis. However, limited research attention has been paid to the dynamic domain-shift …
diagnosis. However, limited research attention has been paid to the dynamic domain-shift …
Bearing remaining useful life prediction using federated learning with Taylor-expansion network pruning
Accurate prediction of bearing remaining useful life (RUL) is essential for machine health
management. In existing data-driven prognostic methods, centralized data resources and …
management. In existing data-driven prognostic methods, centralized data resources and …
A survey of federated learning from data perspective in the healthcare domain: Challenges, methods, and future directions
Recent advances in deep learning (DL) have shown that data-driven insights can be used in
smart healthcare applications to improve the quality of life for patients. DL needs more data …
smart healthcare applications to improve the quality of life for patients. DL needs more data …
A federated distillation domain generalization framework for machinery fault diagnosis with data privacy
Federated learning is an emerging technology that enables multiple clients to cooperatively
train an intelligent diagnostic model while preserving data privacy. However, federated …
train an intelligent diagnostic model while preserving data privacy. However, federated …
Federated domain generalization with global robust model aggregation strategy for bearing fault diagnosis
X Cong, Y Song, Y Li, L Jia - Measurement Science and …, 2023 - iopscience.iop.org
Federated learning ensures the privacy of fault diagnosis by maintaining a decentralized
and local training data approach, eliminating the need to share confidential information with …
and local training data approach, eliminating the need to share confidential information with …
Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud–edge–client learning for image classification
Y Zhao, J Zhang, Y Cao - Knowledge-Based Systems, 2023 - Elsevier
The cloud–edge–client hierarchical computing architecture takes into account the dual
advantages of cloud and edge computing. However, in realistic scenarios, the explosive …
advantages of cloud and edge computing. However, in realistic scenarios, the explosive …