Privacy-Preserving Multi-Institution Learning for Regulated Medical Imaging and Digital Health Platforms

Abstract

Healthcare artificial intelligence development faces fundamental challenges in accessing diverse training datasets while maintaining patient privacy and regulatory compliance across institutional boundaries. Traditional centralized machine learning approaches prove inadequate for healthcare environments due to stringent data protection requirements, contractual constraints, and operational barriers that prevent effective collaboration between hospitals and healthcare networks. This article presents a comprehensive architectural framework for privacy-preserving multi-institution learning that enables collaborative model development while maintaining complete data sovereignty and regulatory compliance. The proposed system integrates federated learning paradigms with secure aggregation protocols, differential privacy mechanisms, and robust governance structures specifically designed for regulated healthcare environments. The architecture employs a four-plane design separating coordination, training, security, and evidence generation functions to ensure sensitive patient information never crosses institutional boundaries during collaborative learning processes. Advanced cryptographic techniques, including threshold cryptography and secret sharing schemes, provide mathematical privacy guarantees even when participants may be compromised or malicious. The framework addresses critical healthcare-specific challenges, including bias mitigation across demographic populations, clinical risk assessment through multi-layered validation protocols, and comprehensive lifecycle management with evidence generation for regulatory compliance. Systematic defense mechanisms protect against adversarial participants through robust aggregation methods and anomaly detection,  ensuring equitable model performance across diverse patient cohorts and clinical contexts. The verification and deployment framework provides end-to-end traceability, cryptographically signed model artifacts, and staged rollout capabilities with comprehensive monitoring for technical and clinical performance. This architectural solution enables healthcare institutions to participate in collaborative artificial intelligence development while satisfying ethical, legal, and regulatory obligations, ultimately facilitating the creation of more effective, equitable, and clinically relevant machine learning systems that serve diverse populations without compromising fundamental privacy rights.