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InfoResearchPeer-reviewed
Enhancing X-Ray Image Classification Through Heterogeneous Federated Learning With Natural Image-Augmented Models
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Summary
This paper presents NatIMG-FL, a federated learning framework for classifying X-ray images across healthcare institutions without sharing sensitive data. It uses natural images as auxiliary supervised data to align feature distributions between natural and X-ray images, and introduces a dual weights-based knowledge transfer method to handle heterogeneous model architectures.