A Federated Learning Architecture for Prostate MRI Image Segmentation

Federated Learning (FL) has emerged as a key paradigm for addressing privacy-preserving machine learning across distributed environments, particularly in sensitive domains such as healthcare. In this work, we present the design and initial implementation of a FL-based pipeline for prostate cancer segmentation from MRI data within the context of the MUSA project. Leveraging the MUSA Cloud Platform, our architecture integrates hospital-level privacy constraints, decentralized training, and robust security measures. We describe the software stack, operational flow, and report preliminary results on a U-Net model trained in a real-world federated scenario. Our approach demonstrates the feasibility and potential of FL in large-scale clinical ecosystems, providing a foundation for the future development of secure and scalable AI-based healthcare solutions.