A5065180456- Privacy-Preserving Technologies in Data
- Digital and Cyber Forensics
- Infrastructure Maintenance and Monitoring
- Imbalanced Data Classification Techniques
- Industrial Vision Systems and Defect Detection
- Advanced Data and IoT Technologies
- Adversarial Robustness in Machine Learning
- Machine Learning and ELM
- Business Process Modeling and Analysis
Nanjing University of Science and Technology
2023
Deep-learning (DL)-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they located across different entities. Federated learning (FL) enables multiple clients collaboratively train shared model with data privacy guaranteed. However, the domain discrepancy and scarcity problems among deteriorate performance global FL model. To tackle these issues, we propose novel framework called representation-encoding-based federated...
The challenge of data scarcity hinders the application deep learning in industrial surface defect classification (SDC), as it's difficult to collect and centralize sufficient training from various entities Industrial Internet Things (IIoT) due privacy concerns. Federated (FL) provides a solution by enabling collaborative global model across clients while maintaining privacy. However, performance may suffer heterogeneity--discrepancies distributions among clients. In this paper, we propose...
Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they located across different entities. Federated learning (FL) enables multiple clients collaboratively train shared model with data privacy guaranteed. However, the domain discrepancy and scarcity problems among deteriorate performance global FL model. To tackle these issues, we propose novel framework called representation encoding-based federated meta-learning...