Enhancing X-Ray Image Classification Through Heterogeneous Federated Learning With Natural Image-Augmented Models

This research presents NatIMG-FL, a framework that improves X-ray image classification using federated learning (a technique where multiple hospitals train an AI model together without sharing sensitive patient data). The framework addresses two key problems: hospitals have limited X-ray image collections, and they may use different AI model architectures. NatIMG-FL solves these issues by using regular natural images as extra training data and introducing a knowledge transfer method that lets different models learn from each other effectively.