  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 concentration.
Federated learning (FL) overcomes the preceding difficulties by utilizing a
centralized aggregate server to disseminate a global learning model.
Simultaneously, the local participant keeps control of patient information,
assuring data confidentiality and security. This article conducts a
comprehensive analysis of the findings on blockchain technology entangled with
federated learning in healthcare. 5.0. The purpose of this study is to
construct a secure health monitoring system in healthcare 5.0 by utilizing a
blockchain technology and Intrusion Detection System (IDS) to detect any
malicious activity in a healthcare network and enables physicians to monitor
patients through medical sensors and take necessary measures periodically by
predicting diseases.
