FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting

The rapid growth of medical imaging data has intensified the need for advanced computational tools to support clinical decision-making. However, centralized approaches to artificial intelligence development raise significant challenges related to privacy, regulation, and generalizability. This paper introduces FedIHRAS (Federated Intelligent Humanized Radiology Analysis System), a privacy-preserving federated learning framework that enables multi-institutional collaboration for chest X-ray analysis. FedIHRAS integrates pathology classification, visual explainability, anatomical segmentation, and automated clinical report generation into a unified system that incorporates adaptive aggregation strategies, heterogeneity, and non-IID distributions. The framework employs multi-layered differential privacy mechanisms and a secure communication infrastructure to ensure compliance with strict healthcare data protection standards. Experimental validation across four large-scale chest radiograph datasets (approximately 874k images) demonstrates that FedIHRAS retains 98.8\% of the diagnostic accuracy of a centralized model (mean AUC-ROC = 0.911 vs. 0.922) and achieves superior generalization to unseen institutions (94.2\% retention). Explainability and interpretability were preserved at near-centralized levels, with expert radiologists rating 94.6\% of attention maps as clinically reliable. Moreover, privacy robustness tests confirm strong resistance against inference and reconstruction attacks. FedIHRAS reduces barriers to collaborative research and mitigates algorithmic bias, ultimately offering a scalable and equitable solution for radiological analysis in real-world healthcare systems.