42 citations · 50 across the 8 of their papers we have counts for
8 papers
Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing
Minh K. Quan, Pubudu N. Pathirana
Cross-slice attack attribution in 6G networks requires identifying causal propagation chains through shared infrastructure in under 100 ms. Existing methods struggle to satisfy thi…
StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting
Minh K. Quan, Pubudu N. Pathirana
Large-batch Contrastive Learning (CL), the foundation of modern representation learning, is fundamentally incompatible with the volatile resource constraints of edge devices. This…
Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
Minh K. Quan, Pubudu N. Pathirana
Cross-slice attack attribution in 6G networks faces the fundamental challenge of distinguishing genuine causal relationships from spurious correlations in shared infrastructure env…
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…
Quantum-Inspired Genetic Algorithm for Robust Source Separation in Smart City Acoustics
Minh K. Quan, Mayuri Wijayasundara, Sujeeva Setunge +1
The cacophony of urban sounds presents a significant challenge for smart city applications that rely on accurate acoustic scene analysis. Effectively analyzing these complex sounds…
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
Minh K. Quan, Mayuri Wijayasundara, Sujeeva Setunge +1
The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-li…
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
Minh K. Quan, Dinh C. Nguyen, Van-Dinh Nguyen +3
Federated Learning is a promising approach for learning from user data while preserving data privacy. However, the high requirements of the model training process make it difficult…
Holistic Survey of Privacy and Fairness in Machine Learning
Sina Shaham, Arash Hajisafi, Minh K Quan +6
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the…