1 code implementation • 11 Oct 2023 • Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham
In particular, We first formulate an optimization problem that harnesses the sampling process to concurrently reduce overfitting while maximizing accuracy.
1 code implementation • 26 Sep 2023 • Minh-Duong Nguyen, Quang-Vinh Do, Zhaohui Yang, Quoc-Viet Pham, Won-Joo Hwang
Recent research efforts on semantic communication have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems.
no code implementations • 29 Sep 2022 • Minh-Duong Nguyen, Quoc-Viet Pham, Dinh Thai Hoang, Long Tran-Thanh, Diep N. Nguyen, Won-Joo Hwang
Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem.
no code implementations • 2 Jun 2022 • Xuan-Tung Nguyen, Minh-Duong Nguyen, Quoc-Viet Pham, Vinh-Quang Do, Won-Joo Hwang
Based on the property of a FL model, we first determine the number of IoT devices participating in the FL process.
1 code implementation • 14 Apr 2022 • Minh-Duong Nguyen, Sang-Min Lee, Quoc-Viet Pham, Dinh Thai Hoang, Diep N. Nguyen, Won-Joo Hwang
Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing.