25 citations · 48 across the 15 of their papers we have counts for
17 papers
Not All Retained Clients Are Equal: Client Selection for Federated Unlearning
Thanh Linh Nguyen, Quoc-Viet Pham
Federated unlearning (FU) aims to remove a designated target client's influence from a trained model without the prohibitive cost of retraining from scratch. Existing methods large…
Harnessing Disagreement: Detecting Correlated Agreement Blindness in Multi-Agent Triage
Shay Seiya McDonnell, Avantika Singh, Quoc-Viet Pham +2
Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety monitoring where…
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…
Trust Region Bayesian Optimization of Annealing Schedules on a Quantum Annealer
Seon-Geun Jeong, Mai Dinh Cong, Minh-Duong Nguyen +3
Quantum annealing (QA) is a practical model of adiabatic quantum computation, already realized on hardware and considered promising for combinatorial optimization. However, its per…
Embedding-Aware Noise Modeling of Quantum Annealing
Seon-Geun Jeong, Mai Dinh Cong, Dae-Il Noh +2
Quantum annealing provides a practical realization of adiabatic quantum computation and has emerged as a promising approach for solving large-scale combinatorial optimization probl…
Communication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation
Le-Tuan Nguyen, Minh-Duong Nguyen, Seon-Geun Jeong +2
With the rapid emergence of foundation models and the increasing need for fine-tuning across distributed environments, Federated Low-Rank Adaptation (FedLoRA) has recently gained s…
Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead
Thanh Linh Nguyen, Marcela Tuler de Oliveira, An Braeken +2
Federated unlearning (FUL) enables removing the data influence from the model trained across distributed clients, upholding the right to be forgotten as mandated by privacy regulat…
ToFU: Transforming How Federated Learning Systems Forget User Data
Van-Tuan Tran, Hong-Hanh Nguyen-Le, Quoc-Viet Pham
Neural networks unintentionally memorize training data, creating privacy risks in federated learning (FL) systems, such as inference and reconstruction attacks on sensitive data. T…
Enhancing Gradient Variance and Differential Privacy in Quantum Federated Learning
Duc-Thien Phan, Minh-Duong Nguyen, Quoc-Viet Pham +1
Upon integrating Quantum Neural Network (QNN) as the local model, Quantum Federated Learning (QFL) has recently confronted notable challenges. Firstly, exploration is hindered over…
Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach
Minh-Duong Nguyen, Quoc-Viet Pham, Nguyen H. Tran +3
In this study, we design a low-complexity and generalized AI model that can capture common knowledge to improve data reconstruction of the channel decoder for semantic communicatio…
Domain Generalization via Pareto Optimal Gradient Matching
Khoi Do, Duong Nguyen, Nam-Khanh Le +3
In this study, we address the gradient-based domain generalization problem, where predictors aim for consistent gradient directions across different domains. Existing methods have…
Improving Generalization in Heterogeneous Federated Continual Learning via Spatio-Temporal Gradient Matching with Prototypical Coreset
Minh-Duong Nguyen, Le-Tuan Nguyen, Quoc-Viet Pham
Federated Continual Learning (FCL) has recently emerged as a crucial research area, as data from distributed clients typically arrives as a stream, requiring sequential learning. T…
Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks
Seon-Geun Jeong, Pham Dang Anh Duc, Quang Vinh Do +7
In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantu…
Multi-Agent Collaboration Mechanisms: A Survey of LLMs
Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen +3
With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reaso…
A Joint Communication and Computation Design for Semantic Wireless Communication with Probability Graph
Zhouxiang Zhao, Zhaohui Yang, Xu Gan +4
In this paper, we delve into the challenge of optimizing joint communication and computation for semantic communication over wireless networks using a probability graph framework.…
Time-Division Based Integrated Sensing, Communication, and Computing in Integrated Satellite-Terrestrial Networks
Xiangming Zhu, Hua Wang, Zhaohui Yang +1
In this paper, we investigate time-division based framework for integrated sensing, communication, and computing in integrated satellite-terrestrial networks. We consider a scenari…
Distortion Resilience for Goal-Oriented Semantic Communication
Minh-Duong Nguyen, Quang-Vinh Do, Zhaohui Yang +2
Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these appr…