1.4k citations · 2.3k across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
Kanishka Ranaweera, Azadeh Ghari Neiat, Xiao Liu +2
Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sh…
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…
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
Kanishka Ranaweera, David Smith, Pubudu N. Pathirana +3
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine lear…
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…
Federated Learning for Industrial Internet of Things in Future Industries
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana +4
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently,…