A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique
Abdur Rehman, Sagheer Abbas, M. A. Khan, Taher M. Ghazal, Khan, Muhammad Adnan, Amir Mosavi

TL;DR
This paper proposes a secure Healthcare 5.0 system that integrates blockchain and federated learning to enhance data privacy, security, and disease prediction in IoMT networks, addressing key challenges in smart healthcare.
Contribution
It introduces a novel combination of blockchain technology with federated learning and intrusion detection to improve security and privacy in Healthcare 5.0 systems.
Findings
Enhanced data privacy through federated learning
Effective detection of malicious activities with IDS
Improved disease monitoring and prediction accuracy
Abstract
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater…
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Taxonomy
TopicsBlockchain Technology Applications and Security
