Evaluation of Client Participation on Federated Learning Scenario for Chest X-Ray Imaging

Chest X-rays are a crucial diagnostic tool, but their interpretation can be time-consuming. Deep learning offers a promising solution, but requires large, well-annotated datasets, which are often limited by privacy issues and data access restrictions. Federated learning (FL) allows hospitals to collaboratively train deep learning models without data sharing, addressing these limitations. In this paper, we propose an FL model based on lightweight ConvNeXt architecture and Federated Averaging to analyze both performance and interpretability as the number of clients increases, while maintaining the same total amount of data in an independent identically distributed (IID) scenario for chest X-ray imaging. The results demonstrate superior performance of the FL models compared to the local one, and the interpretability analysis shows that FL models produce better alignment with ground truth compared to local models. However, both performance and interpretability decline as the number of clients increases. These findings highlight the need for strategies to mitigate performance degradation in high-client scenarios to improve the clinical applicability of FL-based systems.