43 citations · 143 across the 14 of their papers we have counts for
11 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…
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…
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…
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Huy Q. Le, Minh N. H. Nguyen, Chu Myaet Thwal +3
Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private dat…
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Huy Q. Le, Minh N. H. Nguyen, Shashi Raj Pandey +2
In a practical setting, how to enable robust Federated Learning (FL) systems, both in terms of generalization and personalization abilities, is one important research question. It…
Edge-assisted Democratized Learning Towards Federated Analytics
Shashi Raj Pandey, Minh N. H. Nguyen, Tri Nguyen Dang +4
A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary…
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
Minh N. H. Nguyen, Nguyen H. Tran, Yan Kyaw Tun +2
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new…
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
Tra Huong Thi Le, Nguyen H. Tran, Yan Kyaw Tun +4
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it…
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Minh N. H. Nguyen, Shashi Raj Pandey, Tri Nguyen Dang +4
Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that…