23 citations · 26 across the 17 of their papers we have counts for
17 papers
Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts
Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen +1
Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training. A common…
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.…
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Thinh T. H. Nguyen, Khoa D. Doan, Binh T. Nguyen +2
Federated class-incremental learning (FCIL) becomes substantially harder when clients observe different label subsets, progress through tasks at different stages, and provide uneve…
HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
Thinh T. H. Nguyen, Le-Tuan Nguyen, Minh-Duong Nguyen +4
Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficu…
PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers
Ngoc Phan Phuoc Loc, Toan Huynh La Viet, Thanh Tran Khanh +8
The rapid growth in submissions to machine learning venues has strained the scientific peer-review system and intensified interest in LLM-based automated peer reviewers. However, h…
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
Minh-Duong Nguyen, Thien-Thanh Dao, Le-Tuan Nguyen +2
Rehearsal-based continual learning (CL) mitigates catastrophic forgetting by maintaining a subset of samples from previous tasks for replay. Existing studies primarily focus on opt…
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…
FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning
Tuan Nguyen, Sze Jue Yang, Khoa D. Doan +2
Horizontal federated learning (HFL) backdoor audits often summarize model behavior through clean accuracy (CA), mean attack success rate (ASR), or a single known-trigger test. Such…
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
Thinh Dao, Dung Thuy Nguyen, Khoa D Doan +1
Research on backdoor attacks in Federated Learning (FL) has accelerated in recent years, with new attacks and defenses continually proposed in an escalating arms race. However, the…
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…
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape
Tuan Nguyen, Dung Thuy Nguyen, Khoa D Doan +1
Despite the promise of Federated Learning (FL) for privacy-preserving model training on distributed data, it remains susceptible to backdoor attacks. These attacks manipulate model…
Clean-Label Physical Backdoor Attacks with Data Distillation
Thinh Dao, Khoa D Doan, Kok-Seng Wong
Deep Neural Networks (DNNs) are shown to be vulnerable to backdoor poisoning attacks, with most research focusing on digital triggers -- artificial patterns added to test-time inpu…
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen +3
Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns. However, one of the major challenges in th…
Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models
Sze Jue Yang, Chinh D. La, Quang H. Nguyen +4
Backdoor attacks, representing an emerging threat to the integrity of deep neural networks, have garnered significant attention due to their ability to compromise deep learning sys…
Fooling the Textual Fooler via Randomizing Latent Representations
Duy C. Hoang, Quang H. Nguyen, Saurav Manchanda +3
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cau…
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
Quang H. Nguyen, Yingjie Lao, Tung Pham +2
Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with…