FedHeartMRI: Decentralized Anomaly Detection and Classification in Cardiac MRI Using Federated Learning with Client-Level Differential Privacy

Abstract

Timely analysis of cardiac MRI data is essential for early diagnosis and clinical intervention in cardiovascular illness, which continues to be the one world's leading causes of death. However, cross-institutional data sharing is ethically and legally problematic due to the nature of sensitive medical imaging data, which restricts the capacity to train reliable diagnosis models on the variety of the patient groups. A federated DL system for privacy-preserving anomaly detection and classification in cardiac MRI is presented in this work. It is intended to function in various simulated hospital settings without disclosing raw patient data. The suggested system, HeartMRI-FL, combines two deep learning models, an adaptive sub-client splitting mechanism, client-level differential privacy, and a Flower-based federated learning architecture— a supervised classifier for categorizing cardiac conditions and a convolutional autoencoder for unsupervised anomaly detection. Real-time training triggers, prediction queries, and result visualization are provided by a supplementary Angular-based clinical dashboard that interacts with the federated backend over a REST API. The system not only supports popular medical imaging formats like DICOM, NIfTI, and JPEG, but it also demonstrates that substantial diagnostic performance may be achieved without centralizing patient data. The federated approach provides a workable route toward privacy-compliant multi-institutional cardiac imaging analysis by achieving competitive accuracy while upholding formal differential privacy commitments, as confirmed by experimental results.