Blockchain framework with IoT device using federated learning for sustainable healthcare systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) to enhance smart healthcare. The system ensures data privacy and improves disease and intrusion detection for proactive patient monitoring.
Area Of Science
- Smart Healthcare
- Internet of Medical Things (IoMT)
- Healthcare 5.0
Background
- Security and privacy are critical in the rapidly advancing Internet of Medical Things (IoMT).
- Machine learning (ML) and blockchain (BC) enhance Healthcare 5.0, creating Smart Healthcare.
- Centralized data repositories for ML raise privacy and regulatory concerns.
Purpose Of The Study
- To propose a Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS).
- To develop a secure health monitoring system using federated learning (FL) and blockchain.
- To implement an Intrusion Detection System (IDS) for enhanced healthcare network security.
Main Methods
- Federated learning (FL) enables distributed model training while preserving local data control.
- Blockchain technology enhances security, privacy, and interoperability in healthcare systems.
- An Intrusion Detection System (IDS) monitors network traffic for potential threats.
Main Results
- The FBCI-SHS achieved 98.73% for data privacy and security.
- Intrusion detection efficiency reached 97.16%, and disease detection accuracy was 96.425%.
- Proactive healthcare management improved by 98.37%, with 96.74% interoperability.
Conclusions
- The proposed FBCI-SHS is well-suited for secure medical monitoring and proactive healthcare.
- The system effectively addresses privacy concerns through federated learning.
- The integration of ML, BC, and IoMT offers significant advancements for smart healthcare systems.
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