Federated Learning for Privacy- Preserving Clinical Analytics in Multi-Tenant HIPAA Cloud Systems

Abstract

Multi-tenant HIPAA-compliant cloud environments require clinical analytics methods that improve model performance without exposing protected health information across tenant boundaries. This article presents a federated learning framework for privacy-preserving clinical data analytics in distributed healthcare cloud systems. The framework supports tenant onboarding, local clinical model training, protected update exchange, secure aggregation, tenant isolation monitoring, and audit evidence generation. It maps federated learning operations to HIPAA-relevant safeguards, including access governance, minimum necessary data handling, confidentiality protection, tenant isolation, continuous risk monitoring, and audit readiness. Results show that federated model accuracy, privacy preservation score, and tenant isolation integrity improve across clinical training rounds, while workload-level analysis identifies differences in clinical prediction reliability, update anomaly risk, and audit evidence completeness across EHR analytics, imaging metadata, claims review, population health, pharmacy records, and remote monitoring. The study concludes that federated learning becomes more suitable for regulated clinical analytics when model training, privacy control, tenant isolation, and compliance traceability are managed as one cloud-native workflow.