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Ashfak Md Shibli on Muck Rack

Ashfak Md Shibli

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Secure yet fragile: adversarial vulnerabilities of federated vision–language models in medical AI

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By Zarif Hossain, Saika Zaman, Abdur R. Shahid, Zhen Ni, Ahmed Imteaj, Awal Ahmed Fime, Tasfia Zaman Samiha, Ashfak Md Shibli
| Nature Verified
Abstract Vision–Language Models (VLMs) enable powerful multimodal reasoning for medical image analysis, while federated learning allows collaborative training across institutions without sharing patient data. However, the adversarial robustness of federated medical VLMs remains largely unexplored. This work systematically evaluates the vulnerability of CLIP-based VLMs trained with four federated optimization strategies, FedAvg, FedProx, FedPer, and FedBN, on multiple medical datasets.
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