Researchers propose a new Blockchain-Empowered Federated Learning (BCFL) framework to secure EHRs while enabling AI development

A new study published in Scientific Reports proposes a robust solution to the privacy challenges plaguing centralized Electronic Health Record (EHR) systems. Researchers have developed the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework, designed to eliminate single points of failure while enabling secure AI collaboration. The system combines blockchain technology for tamper-proof, decentralized record-keeping with federated learning, allowing hospitals to train shared AI models without ever exchanging raw patient data. To further enhance security, the framework integrates differential privacy and secure multi-party computation. Performance tests using standard datasets revealed impressive results: the model achieved 95.2% accuracy while reducing network latency by 43% compared to traditional methods. Crucially, the system demonstrated high resilience against data poisoning and adversarial attacks, maintaining over 93% accuracy even under active threat conditions. This innovation offers a scalable path forward for healthcare institutions, enabling them to leverage collective data for medical AI breakthroughs while strictly adhering to data sovereignty and patient privacy requirements.

Read the original article at: https://www.nature.com/articles/s41598-025-12225-x

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