Federated Analytics Framework for Privacy-Preserving Multi-Institutional Clinical Trial Data Processing

Abstract

Clinical trials increasingly require collaboration across geographically distributed hospitals and research centers, each managing sensitive patient data within strict regulatory boundaries. Traditional centralized data integration faces significant challenges related to privacy compliance, data sovereignty, and transfer bottlenecks that impede collaborative healthcare innovation. A federated analytics framework addresses these challenges by enabling institutions to perform computations on local data while sharing only aggregated, privacy-preserving results. The proposed architecture leverages federated query processing and distributed model training, integrating Fast Healthcare Interoperability Resources (FHIR) standards with secure multiparty computation and differential privacy mechanisms to ensure compliance with HIPAA, GDPR, and other healthcare governance regulations. Implementation across multiple hospitals participating in cardiovascular treatment trials demonstrates that federated architectures maintain comparable analytical performance to centralized systems while significantly reducing privacy risks and enhancing cross-institutional collaboration. The framework incorporates Apache Airflow for orchestration, addresses schema harmonization challenges, and establishes trust protocols among participating institutions. This advancement in healthcare infrastructure enables real-time, cross-institutional insights while upholding the highest standards of data stewardship, providing pharmaceutical companies, healthcare systems, and data scientists with a scalable blueprint for accelerating clinical discoveries without compromising patient confidentiality.