5 papers
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…
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…
Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
Senura Hansaja Wanasekara, Minh-Duong Nguyen, Xiaochen Liu +2
Generative modeling has become a central paradigm in protein research, extending machine learning beyond structure prediction toward sequence design, backbone generation, inverse f…
Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression
Minh-Duong Nguyen, Senura Hansaja, Le-Tuan Nguyen +4
Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regul…
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
Minh Duong Nguyen, Khanh Le, Khoi Do +4
In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergenc…