Search Results for author: Minh-Duong Nguyen

Found 5 papers, 3 papers with code

Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing

1 code implementation11 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.

Federated Learning

Joint Communication and Computation Framework for Goal-Oriented Semantic Communication with Distortion Rate Resilience

1 code implementation26 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.

Label driven Knowledge Distillation for Federated Learning with non-IID Data

no code implementations29 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.

Federated Learning Knowledge Distillation

HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

1 code implementation14 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.

Federated Learning

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