15 citations · 19 across the 9 of their papers we have counts for
12 papers
Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement
Loc X. Nguyen, Avi Deb Raha, Huy Q. Le +3
Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world models for prediction, and vi…
Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics
Niloy Das, Apurba Adhikary, Sheikh Salman Hassan +4
Crime pattern analysis is critical for law enforcement and predictive policing, yet the surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced da…
Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +6
The shift toward user-customized on-device learning places new demands on wireless systems: models must be trained on diverse, distributed data while meeting strict latency, bandwi…
Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +7
Semantic communication can significantly improve bandwidth utilization in wireless systems by exploiting the meaning behind raw data. However, the advancements achieved through sem…
Robust Federated Learning on Edge Devices with Domain Heterogeneity
Huy Q. Le, Latif U. Khan, Choong Seon Hong
Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices, making it a popular solution for privacy-sensitive applications.…
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…
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…
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…
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…