6 citations · 20 across the 20 of their papers we have counts for
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DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +3
Deep Joint Source-Channel Coding (Deep-JSCC) has emerged as a promising semantic communication approach for wireless image transmission by jointly optimizing source and channel cod…
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…
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…
Security Risks in Vision-Based Beam Prediction: From Spatial Proxy Attacks to Feature Refinement
Avi Deb Raha, Kitae Kim, Mrityunjoy Gain +4
The rapid evolution towards the sixth-generation (6G) networks demands advanced beamforming techniques to address challenges in dynamic, high-mobility scenarios, such as vehicular…
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…
Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Yu Qiao, Apurba Adhikary, Huy Q. Le +3
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to ad…