5 papers
Federated Learning for Cyber Physical Systems: A Comprehensive Survey
Minh K. Quan, Pubudu N. Pathirana, Mayuri Wijayasundara +5
The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliabili…
Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends
Shiying Zhang, Jun Li, Long Shi +4
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production f…
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana +4
In the era of data-driven machine-learning applications, privacy concerns and the scarcity of labeled data have become paramount challenges. These challenges are particularly prono…
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana +4
Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with…
Private Knowledge Sharing in Distributed Learning: A Survey
Yasas Supeksala, Dinh C. Nguyen, Ming Ding +5
The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed the way society operates. Its widespread use has led to the distribution of AI and i…