2 citations · 3 across the 6 of their papers we have counts for
6 papers
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
Hadi Amini, Md Jueal Mia, Yasaman Saadati +6
Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as tex…
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Ervin Moore, Ahmed Imteaj, Md Zarif Hossain +2
Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model gener…
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
Ahmed Imteaj, Md Zarif Hossain, Saika Zaman +1
The rapid advancement and increasing complexity of pretrained models, exemplified by CLIP, offer significant opportunities as well as challenges for Federated Learning (FL), a crit…
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks
Md Zarif Hossain, Ahmed Imteaj
Large Vision-Language Models (LVLMs), trained on multimodal big datasets, have significantly advanced AI by excelling in vision-language tasks. However, these models remain vulnera…
Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models
Md Zarif Hossain, Ahmed Imteaj
Vision-Language Models (VLMs) rely heavily on pretrained vision encoders to support downstream tasks such as image captioning, visual question answering, and zero-shot classificati…
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer
Md Zarif Hossain, Ahmed Imteaj
Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the di…