Blockchain And Artificial Intelligence Enabled Differential Privacy Federated Learning For Secure And Auditable Medical
Keywords:
Federated Learning, Blockchain, Differential Privacy, Medical AI, Healthcare Informatics, Smart ContractsAbstract
The deployment of artificial intelligence in clinical settings is impeded by patient data privacy regulations, institutional data silos, and the lack of verifiable model governance. This paper proposes a Blockchain-enabled Differential Privacy Federated Learning (BC-DPFL) architecture that simultaneously addresses privacy, auditability, and model performance in distributed medical AI systems. The proposed model achieves 94.2% diagnostic accuracy on a multi-institution chest pathology classification task a 22.4 percentage point improvement over centralized baselines restricted by data-sharing barriers—while maintaining provable differential privacy guarantees (ε = 1.2). Blockchain-based gradient aggregation provides tamper-evident model versioning, enabling regulatory audits without exposing patient data. Evaluation across 8 simulated hospital nodes and 45,000 chest radiograph samples demonstrates scalability and statistical robustness.
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