Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management

The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities highlights its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead at edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy preservation mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments—particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount. To address these challenges, this study proposes the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework—a novel architecture that mixes blockchain technology, hybrid privacy mechanisms, and optimized communication strategies. The proposed system features a three-layer design: (1) the Edge Nodes Layer, where client devices perform local model training while retaining raw data; (2) the Federated Model Aggregation Layer, which securely aggregates encrypted updates using Differential Privacy and Secure Multi-Party Computation (SMPC); and (3) the Blockchain Network Layer, which guarantees tamper-proof auditability and trust through a lightweight Proof-of-Stake (PoS) consensus enhanced with Byzantine Fault Tolerance (BFT). Experimental evaluation on the CIFAR-10 dataset demonstrates that EPP-BCFL achieves 95.2% accuracy, significantly reduced communication overhead, and strong resilience against adversarial attacks. Comparative analysis with existing FL models highlights the proposed framework’s superior performance with respect to privacy preservation, computational efficiency, and robust security, making it well-suited for secure, scalable healthcare applications.