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

Summary

Researchers asked whether adding targeted adversarial training to a Federated Learning pipeline improves diagnosis and spatial interpretability in chest X-ray analysis. They trained a ConvNeXt model with Federated Averaging and a single-step adversarial strategy (Step-ML) across controlled and multi-site heterogeneous client setups. The adversarially enhanced approach improved diagnostic performance over non-adversarial and non-targeted baselines, and the aggregated model stayed more robust than individual local models under severe data fragmentation.