Federated learning-based secure electronic health record sharing scheme in medical informatics
Abstract
Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.
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Salim and Park presented a secure EHR scheme in a hospital using federated learning and blockchain, where IPFS stores private data, EHR hashed addresses are recorded on a Consortium Blockchain, access is granted by patients via smart contracts, and EHR data are trained locally and globally.
“Salim and Park [44] presented a secure EHR scheme in a hospital that adopted FL and blockchain technology. In the scheme, Inter Planetary File System (IPFS) stores private data. In the IPFS, all EHR hashed addresses are recorded using a Consortium Blockchain-based network. Access to the EHR is granted by individual patients enabled by smart contracts. EHR data are trained both locally and globally”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [44]
All four FL-chain systems [42,43,44,45] from this review adopted additional privacy enhancement methods to improve privacy protection.
“All of the four FL-chain systems [42,43,44,45] from this review adopted additional privacy enhancement methods to improve privacy protection.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [44]
The consensus process in the FL-chain systems [42,43,44,45] requires significant computational resources.
“Besides the integration of the additional privacy enhancement methods, the consensus process in the FL-chain systems [42,43,44,45] requires significant computational resources.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [44]
Four FL-chain healthcare applications in this review demonstrate that blockchain technology is relevant to FL.
“Four FL-chain healthcare applications [42,43,44,45] in this review prove that the blockchain technology is relevant to FL.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [44]
The existing work in this review presented approaches including adding gradient [42], extra elements [43], hashed EHR address [44], and additional rules [45].
“The existing work in this review presented the approaches including adding gradient [42], extra elements [43], hashed EHR address [44], and additional rules [45].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [44]
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“Salim and Park [167] compared the results of their decentralized Convolutional Neural Network (CNN) model for Electronic Health Records (EHR) with a private Interplanetary File System (IPFS) and found promising results in accuracy, sensitivity, and specificity, similar to the traditional centralized…”
A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
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- Authors
- Salim, M. M., & Park, J. H.
- Affiliations
- Seoul National University of Science and Technology (KR)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2023-02
- Volume
- 27
- Issue
- 2
- Pages
- 617-624
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4280564713
- Cited by
- 80
- References
- 21
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 22 · 2023: 16 · 2022: 2
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Defense Acquisition Program Administration and Agency for Defense Development (UD210029TD)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, electronic health records, data privacy, health data sharing, IPFS, blockchain, smart contracts, convolutional neural networks, decentralized machine learning, clinical research data, medical cyber-physical systems, patient data protection
- MeSH terms
- Blockchain, Artificial Intelligence, Confidentiality, Humans, Medical Informatics, Privacy, Electronic Health Records
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