6 papers · 1 filter
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang +1
Prompt tuning provides a parameter-efficient way to adapt foundation models (FMs) by freezing the pretrained backbone and updating only a small set of learnable prompts. This prope…
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Hoang M. Ngo, Quan Nguyen, Wanli Xing +1
Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low co…
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3
Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard FL, the recent shift toward feder…
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
Hoang M. Ngo, Nhat Hoang-Xuan, Quan Nguyen +3
Quantum Machine Learning (QML) promises significant computational advantages, but preserving training data privacy remains challenging. Classical approaches like differentially pri…
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-…
FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
Hoang M. Ngo, Tamer Kahveci, My T. Thai
Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing…