3 papers
cs.LG2026
Rethinking Molecular Graph Backdoors under Chemistry-aware Admission
Thinh T. H. Nguyen, Sze Jue Yang, Khoa D. Doan +2
Backdoor attacks on molecular graph neural networks (GNNs) are typically evaluated as abstract graph edits, but real molecular learning pipelines do not train on arbitrary graphs.…
cs.LG2025
Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
Thinh Nguyen, Le Huy Khiem, Van-Tuan Tran +3
Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits. However, most methods assume all clien…
cs.LG2025
HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation
Thinh Nguyen, Trung Phan, Binh T. Nguyen +2
Federated Learning (FL) is a decentralized approach where multiple clients collaboratively train a shared global model without sharing their raw data. Despite its effectiveness, co…