109 citations · 336 across the 15 of their papers we have counts for
10 papers · 1 filter
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
Huy Q. Le, Ye Lin Tun, Yu Qiao +4
Federated Learning (FL) has emerged as a decentralized machine learning technique, allowing clients to train a global model collaboratively without sharing private data. However, m…
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…
Attention on Personalized Clinical Decision Support System: Federated Learning Approach
Chu Myaet Thwal, Kyi Thar, Ye Lin Tun +1
Health management has become a primary problem as new kinds of diseases and complex symptoms are introduced to a rapidly growing modern society. Building a better and smarter healt…
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 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…
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
Ye Lin Tun, Chu Myaet Thwal, Ji Su Yoon +3
Diffusion models have shown great potential for vision-related tasks, particularly for image generation. However, their training is typically conducted in a centralized manner, rel…
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Ye Lin Tun, Minh N. H. Nguyen, Chu Myaet Thwal +2
Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suff…
An Efficient Federated Learning Framework for Training Semantic Communication System
Loc X. Nguyen, Huy Q. Le, Ye Lin Tun +4
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication…
Federated Learning based Energy Demand Prediction with Clustered Aggregation
Ye Lin Tun, Kyi Thar, Chu Myaet Thwal +1
To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of c…
Federated Learning with Intermediate Representation Regularization
Ye Lin Tun, Chu Myaet Thwal, Yu Min Park +2
In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their priv…