activity
20242026
most citedA Survey on Foundation Models for Personalized Federated Intelligence

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

math.PR2026

The classical discrete laws near the mode: complete local expansions for the negative binomial and hypergeometric distributions

Neven Elezović

We derive complete local asymptotic expansions near the mode for the negative binomial and hypergeometric laws, complementing the binomial expansion obtained in the companion paper…

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.IT2025

A Contemporary Survey on Semantic Communications:Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding

Loc X. Nguyen, Avi Deb Raha, Pyae Sone Aung +3

Semantic communication is emerging as the next pillar in wireless communication technology due to its transformative capabilities in reducing communication overhead, enhancing robu…

cs.NI2025

A Deep Incremental Framework for Multi-Service Multi-Modal Devices in NextG AI-RAN Systems

Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4

In this paper, we propose a deep incremental framework for efficient RAN management, introducing the Multi-Service-Modal UE (MSMU) system, which enables a single UE to handle eMBB…

cs.CV2025

FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining

Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4

In this paper, we propose the FedFeat+ framework, which distinctively separates feature extraction from classification. We develop a two-tiered model training process: following lo…