109 citations · 336 across the 15 of their papers we have counts for
16 papers
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…
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
Chu Myaet Thwal, Ye Lin Tun, Minh N. H. Nguyen +2
Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resour…
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…
Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation
Loc X. Nguyen, Kitae Kim, Ye Lin Tun +4
Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, a…
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Chu Myaet Thwal, Ye Lin Tun, Kitae Kim +2
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV). The ability to capture long-range dependenci…
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…
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Chu Myaet Thwal, Minh N. H. Nguyen, Ye Lin Tun +3
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of F…
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…
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
Loc X. Nguyen, Ye Lin Tun, Yan Kyaw Tun +4
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication syst…
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
Chaoning Zhang, Chenshuang Zhang, Sheng Zheng +14
As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With su…
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…