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

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Abstract

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.

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Author information

Authors and Affiliations

  1. Applied College, University of Tabuk, Tabuk, Saudi Arabia

    Sami Mnasri

  2. RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, Tunisia

    Dorsaf Salah & Hanen Idoudi

  3. King Faisal University (KFU), Al Ahsa, Saudi Arabia

    Hanen Idoudi

Authors
  1. Sami Mnasri
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  2. Dorsaf Salah
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  3. Hanen Idoudi
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Contributions

This work is part of the work of the Phd student D.S. H.I. as a confirmed professor in his field is the director initiated this work in collaboration with the co-supervisor S.M. from the University of Tabuk. Discussions between the three researchers allow to define the framework of this work. The student then had the task of carrying out experiments after Pr. H.I. and Dr. S.M. checked her code. For the writing, the state of the art section was initiated by the student but the two confirmed professors rewrote and improved this part. Finally, the analysis of the experimental part was the subject of much discussion for the final writing of the text. The two professors then took the paper back to improve the writing and add missing elements. Revision 1 (refinement of manuscript and coding) was achieved mainly by Dr. Sami Mnasri.

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Correspondence to Sami Mnasri.

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Mnasri, S., Salah, D. & Idoudi, H. A hybrid blockchain and federated learning attention-based BERT transformer framework for medical records management. J Supercomput 81, 317 (2025). https://doi.org/10.1007/s11227-024-06816-0

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  • DOI: https://doi.org/10.1007/s11227-024-06816-0

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