Federated Learning and Differential Privacy in Multi-Hospital Healthcare Cloud Ecosystems

Training generalizable deep learning models for clinical diagnostics requires diverse, multi-institutional patient cohorts that no single healthcare provider can assemble in isolation. However, centralizing electronic health records (EHR) and high-resolution medical imaging (DICOM) into a single cloud data lake violates strict regulatory mandates including the Health Insurance Portability and Accountability Act (HIPAA), the European General Data Protection Regulation (GDPR), and institutional data sovereignty policies. Federated Learning (FL) resolves this impasse by dispatching model parameters to hospital edge enclaves for local stochastic training and aggregating only ephemeral gradient updates in the cloud. Despite avoiding raw data egress, vanilla federated averaging (FedAvg) remains vulnerable to gradient inversion reconstruction attacks, membership inference disclosures, and severe statistical divergence across heterogeneous clinical scanners and non-identically distributed (non-IID) patient demographics. This paper presents a production-grade multi-hospital federated learning cloud architecture that integrates Rényi Differential Privacy (RDP) with Poisson subsampled Gaussian noise, Bonawitz cryptographic Secure Aggregation (SecAgg) inside Confidential Computing (AMD SEV-SNP) enclaves, and FedProx/SCAFFOLD control-variate drift correction. We analyze real-world clinical deployments anchored by the NVIDIA Clara Federated Learning and MONAI framework during the 20-hospital EXAM clinical radiology consortium led by Mass General Brigham and Mayo Clinic. Empirical evaluations across 16 simulated and clinical hospital enclaves training a 3D DenseNet-121 chest CT triage model demonstrate that combining FedProx (mu = 0.01) with Opacus RDP accounting (epsilon = 2.5, delta = 10^-5) achieves an area under the receiver operating characteristic curve (AUROC) of 0.918, outperforming isolated single-hospital models (0.784 AUROC) by 17.1% while suppressing Deep Leakage from Gradients (DLG) reconstruction PSNR below 11.4 dB. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Privacy-Preserving Machine Learning and Healthcare Cloud. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.