4 papers
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
Thanh Linh Nguyen, Nguyen Van Huynh, Quoc-Viet Pham
In data-sensitive domains such as healthcare, cross-silo federated learning (CFL) allows organizations to collaboratively train AI models without sharing raw data. However, practic…
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…
A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning
Thanh Linh Nguyen, Quoc-Viet Pham
Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by k…
Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning
Thanh Linh Nguyen, Dinh Thai Hoang, Diep N. Nguyen +1
Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently i…