Data Stays, Knowledge Travels: A Novel Framework for Privacy-Compliant Healthcare AI

The advancement of artificial intelligence in healthcare relies heavily on access to large, diverse datasets, yet privacy regulations and institutional data silos create significant barriers to centralized data collection. This article presents a privacy-preserving federated learning framework tailored for multi-institutional healthcare environments. The proposed architecture incorporates differential privacy, homomorphic encryption, and secure aggregation to ensure data confidentiality throughout the learning process. A novel Federated Transfer Learning component addresses data heterogeneity across institutions, while a reputation-based aggregation mechanism dynamically adjusts institutional contributions based on update quality. The framework was evaluated using real-world healthcare datasets for sepsis prediction and diabetic retinopathy classification, demonstrating that federated models can achieve performance comparable to centralized approaches while preserving patient privacy. Performance analysis confirmed the system's efficiency across varying computational resources and bandwidth constraints. Implementation considerations include technical infrastructure requirements, governance structures for intellectual property and participation standards, and regulatory compliance mechanisms. This article demonstrates the viability of secure, collaborative AI in healthcare while maintaining patient trust and regulatory compliance.