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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A Blockchain-Based Federated Learning Method for Smart Healthcare.

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This study introduces a secure smart healthcare solution using blockchain and federated learning for Medical Internet of Things (MIoT) devices. It enhances data privacy and resists attacks, ensuring reliable healthcare services.

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Area of Science:

  • Smart Healthcare
  • Artificial Intelligence
  • Data Security

Background:

  • The rise of AI and epidemics necessitates smart healthcare solutions.
  • Medical Internet of Things (MIoT) faces challenges in data privacy, security, and service quality.
  • Federated learning and blockchain offer potential solutions for these issues.

Purpose of the Study:

  • To present a novel blockchain-based federated learning method for smart healthcare.
  • To address data privacy, malicious attacks, and service quality concerns in MIoT.
  • To enhance the security and reliability of smart healthcare systems.

Main Methods:

  • Implemented a blockchain network where edge nodes maintain the ledger to prevent single points of failure.
  • Utilized federated learning across MIoT devices to leverage distributed clinical data.
  • Designed an adaptive differential privacy algorithm for robust data privacy protection.
  • Developed a gradient verification-based consensus protocol to detect and mitigate poisoning attacks.

Main Results:

  • The proposed method achieved high model accuracy on a real-world diabetes dataset.
  • Experimental results demonstrated acceptable running times for the system.
  • The approach effectively reduced privacy budget consumption.
  • The system showed strong performance in resisting poisoning attacks.

Conclusions:

  • The blockchain-based federated learning method provides a secure and efficient solution for smart healthcare.
  • The integration of adaptive differential privacy and gradient verification enhances data protection and system integrity.
  • This approach offers a promising direction for developing resilient and trustworthy smart healthcare systems.