Federated intelligence for intrusion detection and unifying health prediction in IoHT
A. Noor, Tasleema Noor · IET conference proceedings. · 2024
In this paper, a method for protecting the privacy of private health information called information-fused blockchainbased Federated Machine Learning-based disease forecasting utilizing Intrusion Detection System (IDS) on Internet of Health Things (IoHT) is presented. Privacy and secrecy are the main IoHT problems because of the size and extensive implementation of IoHT devices. The development of ``Smart Healthcare'' is a result of Machine Learning (ML) strategy, which has greatly improved the capacities and amenities of healthcare 5.0. As a consequence, patients' quality of life will increase, and stress levels and medical costs will decrease. Among the data-driven technological characteristics made feasible by the IoHT is smart, collaborative healthcare. However, combining healthcare information into a single storage site is necessary to create a potent ML model, which creates security, regulatory and ownership difficulties. By propagating a universal framework for learning via a centralized aggregate service, Federated Learning (FL) addresses these problems. The local strategy maintains control over the patient data, preserving its privacy and confidentiality. The following advantages of the recommended course of action: (1) This paper carefully examines the use of blockchain-based FL for healthcare 5.0. (2) The technique aims to provide a secure healthcare surveillance system for healthcare 5.0 by using an IDS to detect any malicious behavior in the healthcare industry. (3) The proposed system performs more effectively for forecasting intrusion detection than the healthcare system linked to the FL approach suggested in the literature.