Federated Deep Learning Architecture for Privacy-Preserving Medical Image Segmentation Across Distributed Hospitals.

Medical image segmentation plays a fundamental role in computer-aided diagnosis, treatment planning, surgical navigation, radiotherapy guidance, and longitudinal disease monitoring. However, developing robust deep learning segmentation models typically requires access to large and diverse medical imaging datasets collected from multiple healthcare institutions. In real-world clinical environments, direct centralization of medical images is often restricted due to patient privacy regulations, institutional data-governance policies, ethical constraints, and concerns related to data ownership. These limitations are particularly challenging in multi-hospital settings, where imaging data are distributed across heterogeneous sites with different scanners, acquisition protocols, patient populations, and annotation standards. To address these challenges, this paper proposes a federated deep learning architecture for privacy-preserving medical image segmentation across distributed hospitals, enabling collaborative model training without transferring raw patient data outside local clinical institutions.

The proposed framework adopts a decentralized federated learning paradigm in which each participating hospital trains a local segmentation model using its own private medical image repository, while only encrypted model updates or gradient parameters are communicated to a secure central aggregation server. The segmentation backbone is based on advanced encoder–decoder architectures, including U-Net, Attention U-Net, and TransUNet, which are designed to capture both local anatomical structures and global contextual representations. To improve robustness under non-independent and identically distributed data conditions, the proposed architecture integrates adaptive federated aggregation strategies such as FedAvg, FedProx, and FedAdam, allowing the global model to learn from heterogeneous institutional data while reducing client drift and convergence instability.

A comprehensive preprocessing pipeline is incorporated at each hospital site, including image normalization, spatial resizing, noise reduction, intensity standardization, data augmentation, and expert-annotated mask alignment. The framework is designed for multiple segmentation tasks, including brain tumor segmentation from MRI, liver and kidney lesion segmentation from CT scans, and skin lesion segmentation from dermoscopic images. To strengthen privacy protection, the proposed system integrates secure aggregation, differential privacy, gradient clipping, and optional encrypted communication protocols to reduce the risk of sensitive information leakage from shared model updates. Furthermore, the architecture supports asynchronous client participation, allowing hospitals with different computational resources and network conditions to contribute to training without disrupting the global optimization process.

The proposed federated segmentation model is evaluated using multi-institutional and publicly available medical imaging datasets under simulated distributed hospital scenarios. Performance is assessed using clinically relevant segmentation metrics, including Dice Similarity Coefficient, Intersection over Union, sensitivity, specificity, Hausdorff Distance, and boundary F1-score. Experimental evaluation demonstrates that the proposed federated framework achieves segmentation performance comparable to centralized training while preserving institutional data privacy. Compared with standalone local training, the federated model improves generalization across unseen hospital domains and produces more consistent segmentation boundaries under heterogeneous imaging conditions. In addition, privacy-aware training mechanisms show a favorable balance between segmentation accuracy, communication efficiency, and patient data protection.

The proposed architecture provides a scalable, secure, and clinically applicable solution for collaborative medical image analysis across distributed healthcare institutions. By enabling hospitals to jointly train high-performance segmentation models without exposing raw patient images, the framework addresses one of the major barriers to real-world medical AI deployment. Its privacy-preserving design, compatibility with heterogeneous hospital infrastructures, and ability to support explainable and auditable model development make it a promising foundation for next-generation computer-aided diagnosis systems, particularly in multi-center clinical studies and resource-constrained healthcare networks.