14 papers
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…
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…
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…