Blockchain-IoMT-enabled federated learning: An intelligent privacy-preserving control policy for electronic health records

The integration of the Internet of Medical Things (IoMT), blockchain technology, and federated learning can provide a new approach to keeping Electronic Health Records (EHRs) in a decentralized, secure, and privacy-protecting form. This article introduces a novel Blockchain-IoMT-based Federated Learning (FL) system that uses a smart privacy-preserving control method to solve the key problems in EHR administration, including data security, patient privacy, and interoperability. The FL paradigm limits patient data to edge nodes, limiting the opportunities of centralized attacks. Although advanced privacy-sensitive methods, such as differential privacy and homomorphic encryption, ensure that the sensitive data is not exposed to adversarial models during training and communication, blockchain technology allows recording the data immutably and auditing it transparently, as well as decentralizing data access. Experimental evaluation with the Parkinson disease data indicates that the proposed PPFL-ICP (Privacy-Preserving Federated Learning with Intelligent Control Policy) model is superior to the current practices in accuracy, robustness, and computational efficiency. The results confirm the usefulness of the framework in protecting healthcare data, enabling secure communication among the spread nodes, and setting the stage of scalable and privacy-aware healthcare systems.