Hospital Participation in Federated Learning: Evaluating Sustainability and Clinical Utility

Abstract

Prostate cancer (PCa) diagnosis often relies on biopsies, which can lead to unnecessary procedures and complications. Federated learning (FL) offers a privacy-preserving approach for training predictive models across hospitals without sharing sensitive patient data. In this study, we evaluate the feasibility of FL for PCa risk prediction by benchmarking different training strategies, including local, federated models, as well as free-riding (FR) on federated models. Using real-world heterogeneous datasets from 19 hospitals, we analyze the impact of data diversity and consortium size on predictive performance. Our results show that while FL improves model generalizability, local models often perform comparably, making direct participation in FL less beneficial for large hospitals. However, a small consortium of high-data-quality institutions could collaboratively develop robust models for broader clinical use. We discuss the practical implications of FL in healthcare and propose strategies for sustainable deployment in real-world hospital networks.

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