works on

From the 1 of 9 linked papers with an AI index.

activity
20242026
collaborators

9 papers

cs.LG2026

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 paper proposes a VAE‑based multi‑task semantic communication system for satellite‑assisted 6G connected autonomous vehicles, jointly handling traffic sign reconstruction and cl…

cs.AI2026

A Survey on Foundation Models for Personalized Federated Intelligence

Yu Qiao, Huy Q. Le, Avi Deb Raha +7

The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remark…

cs.LG2026

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…

cs.CV2026

FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

Huy Q. Le, Loc X. Nguyen, Yu Qiao +3

Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensitive applications. However, in…

cs.LG2025

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…

cs.LG2025

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…