4 citations · 4 across the 6 of their papers we have counts for
6 papers
Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms
Hoang M. Ngo, Tre' R. Jeter, Incheol Shin +3
Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as classification. Although quantum no…
QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid
Hoang M. Ngo, Tre' R. Jeter, Jung Taek Seo +1
Smart grid infrastructures have revolutionized energy distribution, but their day-to-day operations require robust anomaly detection methods to counter risks associated with cyber-…
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu +3
Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding o…
Analysis of Privacy Leakage in Federated Large Language Models
Minh N. Vu, Truc Nguyen, Tre' R. Jeter +1
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need…
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
Tre' R. Jeter, Truc Nguyen, Raed Alharbi +1
Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its us…
Blockchain-based Secure Client Selection in Federated Learning
Truc Nguyen, Phuc Thai, Tre' R. Jeter +2
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local…