Federated Data Processing Architectures for Secure Cross-Organization Analytics

Abstract

The growing volume of data generated across enterprises, healthcare institutions, financial organizations, research centers, and government agencies has created significant opportunities for cross-organization analytics. Collaborative analysis of distributed datasets can improve decision-making, predictive modeling, operational efficiency, and scientific discovery. However, traditional centralized data-sharing approaches often face substantial challenges related to privacy protection, regulatory compliance, data ownership, cybersecurity risks, and organizational trust. Regulations such as GDPR and other data governance frameworks further restrict the unrestricted movement of sensitive information across institutional boundaries. Federated data processing architectures have emerged as a promising solution to these challenges by enabling organizations to collaboratively perform analytics and machine learning while keeping data locally stored. Instead of transferring raw datasets, federated systems exchange model parameters, aggregated statistics, or encrypted computations, thereby reducing privacy risks and enhancing data security. Recent advances in federated learning, secure multi-party computation, differential privacy, homomorphic encryption, and secure aggregation have strengthened the feasibility of secure cross-organization analytics. This study evaluates a federated data processing architecture for secure cross-organization analytics using a simulated multi-organization experimental setup. The findings indicate that federated approaches significantly improve privacy preservation, regulatory compliance, and data utilization while maintaining analytical accuracy. The study highlights current challenges related to scalability, communication overhead, interoperability, and trust management, and identifies future research directions for building secure, efficient, and scalable federated analytics ecosystems.