most citedA Survey on Foundation Models for Personalized Federated Intelligence

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.IT2026

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…

cs.IT2026

A Comprehensive Survey on Semantic Communication in Non-Terrestrial Networks: Architectures, Methodologies, and Challenges

Loc X. Nguyen, Avi Deb Raha, Huy Q. Le +3

The sixth-generation wireless networks are envisioned to deliver ubiquitous, seamless, and intelligent connectivity that reaches far beyond the limits of terrestrial infrastructure…

cs.AI20261 cited

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.IT2026

Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders

Loc X. Nguyen, Phuong-Nam Tran, Trung Thanh Pham +4

Semantic communication (SemCom) is accelerating its momentum to catch up with the massive increase in users' demands in both quantity and quality, with the assistance of advanced d…

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.CV2026

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…