Federated Learning Architectures for Cross-Cloud Healthcare Data Integration: An Empirical Study on Privacy-Preserving EHR Harmonization

Abstract

The ability to share Electronic Health Records across all healthcare providers that function within a siloed data landscape is still an issue for many healthcare providers today. Federated learning offers a new way to conduct shared machine-learning methods through the secure management of patient data that meets all regulatory compliance standards. Traditional centralized approaches require sensitive medical data relocation. This creates substantial security vulnerabilities. It also violates data protection regulations. The proposed architecture enables distributed model training across heterogeneous cloud platforms. Individual patient records remain unexposed throughout the process. Healthcare organizations maintain complete data sovereignty. They simultaneously contribute to shared predictive models. A few examples of ways to protect patient data are through differential privacy, secure contract aggregation, and homomorphic encryption. These protect against various attack vectors targeting patient information. Cross-cloud deployment addresses technical complexities. These arise from diverse infrastructure platforms, authentication systems, and network configurations. Electronic Health Record harmonization transforms disparate clinical data formats. It creates standardized representations suitable for machine learning applications. Empirical validation demonstrates something important. Federated approaches achieve comparable accuracy to centralized baselines. They maintain strict privacy guarantees simultaneously. Communication efficiency optimizations reduce bandwidth requirements. This makes deployment feasible across institutions with limited network capacity. Computational overhead measurements quantify the processing costs. These are associated with cryptographic protections. Scalability testing confirms that system performance improves consistently. This happens as additional healthcare organizations join federated networks. The framework establishes foundational principles. These support deploying privacy-preserving collaborative learning in clinical environments. The deployment satisfies HIPAA and GDPR regulatory mandates.