[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis
The mainstream approach to addressing the issues of insufficient historical data and high
annotation costs in the domain of rotating machinery is to build transfer learning models …
annotation costs in the domain of rotating machinery is to build transfer learning models …
Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains
Leveraging distributed data from various clients to tackle target issues has become a
prominent trend in fault diagnosis. However, the growing concerns about data privacy have …
prominent trend in fault diagnosis. However, the growing concerns about data privacy have …
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 …
Intelligent fault diagnosis via ring-based decentralized federated transfer learning
L Wan, J Ning, Y Li, C Li, K Li - Knowledge-Based Systems, 2024 - Elsevier
Federated transfer learning (FTL) can effectively address the data silos and domain shift that
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …
exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for …
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 …
Artificial intelligence and edge computing for machine maintenance-review
Industrial internet of things (IIoT) has ushered us into a world where most machine parts are
now embedded with sensors that collect data. This huge data reservoir has enhanced data …
now embedded with sensors that collect data. This huge data reservoir has enhanced data …
Modeling of the hysteretic behavior of nonlinear particle damping by Fourier neural network with transfer learning
The particle damper (PD) filled with granular material exhibits hysteretic behavior under
dynamic excitation, meaning that its response depends not only on the current excitation but …
dynamic excitation, meaning that its response depends not only on the current excitation but …
Cloud-edge collaborative transfer fault diagnosis of rotating machinery via federated fine-tuning and target self-adaptation
The data-driven fault diagnostic methods have made significant advances and
breakthroughs in the past decades. However, due to the huge time and labor costs, single …
breakthroughs in the past decades. However, due to the huge time and labor costs, single …
Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis
Operators from various industries have been pushing the adoption of wireless sensing
nodes for industrial monitoring, and such efforts have produced sizeable condition …
nodes for industrial monitoring, and such efforts have produced sizeable condition …