Improving diagnosis and interpretability in chest X-rays through federated learning and adversarial training

Chest X-ray imaging is the most common radiological test for diagnosing a wide range of lung conditions. Deep Learning (DL) has emerged as a powerful tool for automated analysis of chest X-rays, but it requires large, well-annotated datasets that are often difficult to obtain due to privacy concerns and institutional data restrictions. Federated Learning (FL) addresses this by allowing hospitals to collaboratively train DL models without data sharing. Despite its benefits, achieving robust and balanced generalization across heterogenous data in decentralized settings remains a challenge. We investigate how integrating Adversarial Attacks, which have shown potential in mitigating biases in data skewed regimes, into an FL pipeline impacts both performance and spatial interpretability in chest X-ray analysis.We analyze the viability of an Adversarial Attacks-enhanced FL framework in two simplified …