Blockchain framework with IoT device using federated learning for sustainable healthcare systems
Abstract The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically impro...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06539-z |
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| author | B. Bhasker P. Muralidhara Rao P. Saraswathi S. Gopal Krishna Patro Javed Khan Bhutto Saiful Islam Mohammed Kareemullah Addisu Frinjo Emma |
| author_facet | B. Bhasker P. Muralidhara Rao P. Saraswathi S. Gopal Krishna Patro Javed Khan Bhutto Saiful Islam Mohammed Kareemullah Addisu Frinjo Emma |
| author_sort | B. Bhasker |
| collection | DOAJ |
| description | Abstract The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare. A proactive healthcare system may prevent long-term harm by recognizing issues early. This would improve patients’ quality of life while alleviating their worry and healthcare expenses. The IoMT facilitates several capabilities in information technology, including intelligent and interactive healthcare. Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. Hence, this study proposes the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) for a secure health monitoring system. Additionally, this paper presents the Intrusion Detection System (IDS) as a tool for healthcare network intrusion detection, allowing doctors to track patients’ vitals using medical sensors and anticipate when they could become sick so they can take preventative steps. The suggested system proves that the method is well-suited for medical monitoring. In contrast, the high prediction accuracy for intrusion detection and the high efficiency in disease detection achieved by the proposed FBI-SHS healthcare 5.0 system. The proposed method achieves data privacy and security by 98.73%, intrusion detection efficiency by 97.16%, disease detection accuracy by 96.425, proactive healthcare management by 98.37%, and interoperability by 96.74%. |
| format | Article |
| id | doaj-art-07deb72d2f1f4aac9d71dca1738bdf08 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-07deb72d2f1f4aac9d71dca1738bdf082025-08-20T03:42:25ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-06539-zBlockchain framework with IoT device using federated learning for sustainable healthcare systemsB. Bhasker0P. Muralidhara Rao1P. Saraswathi2S. Gopal Krishna Patro3Javed Khan Bhutto4Saiful Islam5Mohammed Kareemullah6Addisu Frinjo Emma7School of Computing and Information Technology, REVA UniversitySchool of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering for WomenSchool of Technology, GITAM UniversitySchool of Engineering, Sreenidhi UniversityDepartment of Electrical Engineering, College of Engineering, King Khalid UniversityCollege of Engineering, King Khalid UniversityDepartment of Mechanical Engineering, Graphic Era (Deemed to be University)College of Engineering and Technology, Dilla University Gedeo Zone, South Ethiopia Regional StateAbstract The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare. A proactive healthcare system may prevent long-term harm by recognizing issues early. This would improve patients’ quality of life while alleviating their worry and healthcare expenses. The IoMT facilitates several capabilities in information technology, including intelligent and interactive healthcare. Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. Hence, this study proposes the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) for a secure health monitoring system. Additionally, this paper presents the Intrusion Detection System (IDS) as a tool for healthcare network intrusion detection, allowing doctors to track patients’ vitals using medical sensors and anticipate when they could become sick so they can take preventative steps. The suggested system proves that the method is well-suited for medical monitoring. In contrast, the high prediction accuracy for intrusion detection and the high efficiency in disease detection achieved by the proposed FBI-SHS healthcare 5.0 system. The proposed method achieves data privacy and security by 98.73%, intrusion detection efficiency by 97.16%, disease detection accuracy by 96.425, proactive healthcare management by 98.37%, and interoperability by 96.74%.https://doi.org/10.1038/s41598-025-06539-zBlockchainInternet of things (IoT)Federated learningSustainable healthcare systemsIntrusion detection systemSmart healthcare |
| spellingShingle | B. Bhasker P. Muralidhara Rao P. Saraswathi S. Gopal Krishna Patro Javed Khan Bhutto Saiful Islam Mohammed Kareemullah Addisu Frinjo Emma Blockchain framework with IoT device using federated learning for sustainable healthcare systems Scientific Reports Blockchain Internet of things (IoT) Federated learning Sustainable healthcare systems Intrusion detection system Smart healthcare |
| title | Blockchain framework with IoT device using federated learning for sustainable healthcare systems |
| title_full | Blockchain framework with IoT device using federated learning for sustainable healthcare systems |
| title_fullStr | Blockchain framework with IoT device using federated learning for sustainable healthcare systems |
| title_full_unstemmed | Blockchain framework with IoT device using federated learning for sustainable healthcare systems |
| title_short | Blockchain framework with IoT device using federated learning for sustainable healthcare systems |
| title_sort | blockchain framework with iot device using federated learning for sustainable healthcare systems |
| topic | Blockchain Internet of things (IoT) Federated learning Sustainable healthcare systems Intrusion detection system Smart healthcare |
| url | https://doi.org/10.1038/s41598-025-06539-z |
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