A hybrid blockchain and federated learning attention-based BERT transformer framework for medical records management

The integration of federated learning, attention-based models like BERT, and blockchain technology presents a transformative approach for managing medical records. This paper introduces a hybrid framework combining the latter technologies to solve critical challenges pertaining to the secure management of healthcare data. Federated learning provides a distributed learning of machine models, where sensitive patient data does not need to be transferred, while BERT models improve the precision in processing medical records using natural language understanding. Blockchain adds a layer of security by recording model updates transparently to ensure tamper-proofing and transactions. A concrete methodology for the implementation of the introduced framework including the design of the smart contract in Solidity is provided to secure recording the model updates. Various tests assessing the performance of the proposed system show a significant improvement in data privacy, model security and precision, compared to the other systems. This hybrid methodology offers advances in handling medical records and elaborates a new benchmark in integrating AI and blockchain for healthcare. This framework thus redefines secure and collaborative healthcare data management, setting the stage for further enhancements in privacy-focused AI applications in medical contexts.