activity
20222024
most citedFedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training

12 citations · 20 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024★ 2 cited

Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning

Son Nguyen, Thinh Nguyen, Khoa D Doan +1

Federated Learning (FL) is a distributed machine learning approach that maintains data privacy by training on decentralized data sources. Similar to centralized machine learning, F…

cs.LG2023★ 4 cited

An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity

Kok-Seng Wong, Manh Nguyen-Duc, Khiem Le-Huy +3

Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications. However, due to…

cs.CV2023

FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection

Thuy Dung Nguyen, Anh Duy Nguyen, Kok-Seng Wong +4

Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, e…

cs.LG2023★ 1 cited

Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions

Thuy Dung Nguyen, Tuan Nguyen, Phi Le Nguyen +3

Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy. However, the heterogeneous distribution o…

cs.LG2023★ 1 cited

Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning

Van-Tuan Tran, Huy-Hieu Pham, Kok-Seng Wong

Federated learning (FL) is recently surging as a promising decentralized deep learning (DL) framework that enables DL-based approaches trained collaboratively across clients withou…

cs.CV2022★ 12 cited

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training

Quan Nguyen, Hieu H. Pham, Kok-Seng Wong +3

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL app…