2 citations · 3 across the 5 of their papers we have counts for
5 papers
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.…
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…
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…
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator
Thinh Nguyen, Khoa D Doan, Binh T. Nguyen +2
Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global mo…
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning
Son Nguyen, Thinh Nguyen, Khoa D Doan +1
Federated Learning (FL) is a distributed machine learning approach that maintains data privacy by training on decentralized data sources. Similar to centralized machine learning, F…