5 papers
A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System
Luyao Zou, Hayoung Oh, Chu Myaet Thwal +3
With the development of wireless network, Multi-Access Edge Computing (MEC) and Artificial Intelligence (AI)-native Radio Access Network (RAN) have attracted significant attention.…
Resource-efficient Layer-wise Federated Self-supervised Learning
Ye Lin Tun, Chu Myaet Thwal, Huy Q. Le +3
Many studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw data distributed across edge devices. However, edge devices often strugg…
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
Huy Q. Le, Chu Myaet Thwal, Yu Qiao +4
Multimodal federated learning (MFL) has emerged as a decentralized machine learning paradigm, allowing multiple clients with different modalities to collaborate on training a globa…
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training
Ye Lin Tun, Chu Myaet Thwal, Minh N. H. Nguyen +1
Combining different data modalities enables deep neural networks to tackle complex tasks more effectively, making multimodal learning increasingly popular. To harness multimodal da…
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
Luyao Zou, Yu Min Park, Chu Myaet Thwal +3
Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an…