Federated Learning for Privacy-Preserving Medical Image Analysis: A Multi-Institutional Framework for Collaborative AI in Healthcare

The application of artificial intelligence in medical imaging has shown tremendous potential for improving diagnosis, treatment planning, and patient outcomes. However, developing robust AI models requires large, diverse datasets that are often siloed across multiple healthcare institutions due to privacy concerns, regulatory constraints, and competitive considerations. This paper presents a comprehensive federated learning framework for privacy-preserving medical image analysis that enables collaborative model development without sharing sensitive patient data. Our approach incorporates advanced federated optimization techniques, secure aggregation protocols, differential privacy mechanisms, and adaptive model architecture to address the unique challenges of medical imaging data. We evaluate the framework on three multi-institutional datasets spanning different imaging modalities: brain MRI tumor segmentation (۱۰ institutions, ۲,۵۰۰ patients), chest X-ray classification (۱۵ institutions, ۱۱۲,۰۰۰ images), and histopathology image analysis (۸ institutions, ۴۵,۰۰۰ slides). Results demonstrate that our federated framework achieves diagnostic accuracy within ۲.۳% of centralized training while providing formal privacy guarantees and reducing communication overhead by ۷۳% compared to baseline federated approaches. Furthermore, the system demonstrates robustness to statistical heterogeneity across institutions and maintains performance even with imbalanced data distributions. Through comprehensive security analysis and deployment case studies at five healthcare systems, we demonstrate that our framework enables practical, secure, and effective collaborative AI development for medical imaging. The proposed approach represents a significant step toward breaking down data silos in healthcare while maintaining the highest standards of patient privacy and data protection.

کلیدواژه ها:

federated learning ، medical imaging ، privacy-preserving AI ، multi-institutional collaboration ، differential privacy ، distributed machine learning ، healthcare AI