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 attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a client's private dataset contains a…
cs.LG2025
Revisiting Kernel Attention with Correlated Gaussian Process Representation
Long Minh Bui, Tho Tran Huu, Duy Dinh +2
Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrat…