144 citations · 442 across the 24 of their papers we have counts for
26 papers
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…
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…
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…
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…
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…
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…
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…
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
Yu Qiao, Apurba Adhikary, Kitae Kim +3
Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data…
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +2
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Gener…
A Complete Survey on LLM-based AI Chatbots
Sumit Kumar Dam, Choong Seon Hong, Yu Qiao +1
The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbot…
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…
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
Yu Qiao, Huy Q. Le, Mengchun Zhang +3
Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge…
Towards Robust Federated Learning via Logits Calibration on Non-IID Data
Yu Qiao, Apurba Adhikary, Chaoning Zhang +1
Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent stud…
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…
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Chaoning Zhang, Yu Qiao, Shehbaz Tariq +5
In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently,…
Generative AI-driven Semantic Communication Framework for NextG Wireless Network
Avi Deb Raha, Md. Shirajum Munir, Apurba Adhikary +2
This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts th…
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…
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Chaoning Zhang, Dongshen Han, Yu Qiao +4
Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like im…
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Yu Qiao, Chaoning Zhang, Taegoo Kang +3
Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance…
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
Chaoning Zhang, Joseph Cho, Fachrina Dewi Puspitasari +11
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentat…
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Dongsheng Han, Chaoning Zhang, Yu Qiao +5
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of…
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Chaoning Zhang, Chenshuang Zhang, Chenghao Li +13
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence…
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…