3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Pei-Yau Weng, Minh Hoang, Lam M. Nguyen +3
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffect…