144 citations · 442 across the 24 of their papers we have counts for
10 papers · 1 filter
A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles
S. M. Abtahiul Alam, Niloy Das, Apurba Adhikary +3
The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies. Future connect…
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…
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
Yu Qiao, Phuong-Nam Tran, Ji Su Yoon +4
Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have attracted widespread attention for their remarkable capabilities in mult…
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…
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
Yu Qiao, Chaoning Zhang, Apurba Adhikary +1
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability t…
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…
Boosting Federated Learning Convergence with Prototype Regularization
Yu Qiao, Huy Q. Le, Choong Seon Hong
As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data.…
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…
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
Yu Qiao, Md. Shirajum Munir, Apurba Adhikary +4
Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution…
Prototype Helps Federated Learning: Towards Faster Convergence
Yu Qiao, Seong-Bae Park, Sun Moo Kang +1
Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heteroge…