## 1 Introduction  
The Internet of Things (IoT) enables the connectivity of physical objects and computational power so they may connect to the Internet. The IoT has the potential to assist in the development of applications that are both adaptable and efficient across a variety of industries, including healthcare, environmental monitoring, and industrial control systems. The IoT in the healthcare environment has led to the establishment of the Internet of Medical Things (IoMT), a cutting-edge area of a cyber physical system for wellness and wellbeing. Integrating these solutions into the HSP system has the potential to improve care services, quality of life, and open the door to cost-effective solutions [1]. For further analysis, the biomedical information pertaining to people is obtained. This information includes medical records, photographs, physiological signals, and many more forms. Given that the IoMT's cyber physical system collects data from several users, the volumetric scale of this biomedical data is enormous [2]. Smartwatches, wristbands, and other wearable sensing devices, among others, help in early illness diagnosis and warning. These wearable devices include strong and application-specific computational architecture that is housed in a distant HSP cloud data center, enhancing their capabilities (for real-time and early detection of health concerns). In IoMT-based healthcare solutions, wearable devices are often collected at the HSP's data center with the goal of preventing, diagnosing, and treating a variety of human health-related issues including cardiovascular illnesses (CVD). The construction of an effective electronic healthcare infrastructure is difficult due to the vast number of data that are gathered from multiple sources, including end users and other stakeholders in the delivery of health services. According to World Health Organization (WHO) projections, CVD-related mortality accounts for close to 18 million fatalities annually globally [3]. Numerous risk factors, such as a history of heart attack, obesity, stress, high blood pressure, smoking, excessive use of alcohol, and high cholesterol, can all contribute to CVD. CVD impairs heart function and results in issues including strokes and impaired blood vessel function. The ability to treat CVD effectively and quickly is crucial for patient survival. The academic and industry communities are paying close attention to machine learning (ML)-based approaches for the accurate detection and prognosis of cardiac disorders. In [4], for example, a hybrid strategy based on a random forest and linear model was proposed to increase heart disease prediction accuracy. Authors in [5] proposed a feature selection and classification technique for identifying cardiac illness in an e-healthcare system. To create an effective prediction model for tracking a patient's health state, traditional ML models are trained on vast amounts of user data. Although it is organized by several autonomous HSPs, these healthcare data are available in scattered, isolated silos. Even if there are a lot of aggregate data in different businesses, sharing the data is limited because of worries about security and privacy. Similarly, collected user data from the crowd are too restricted. These restrictions are enforced through regulatory laws such as the European Union's GDPR [6], China Cyber Security Law [7], and the United States' CCPA of 2018 [8]. Hence, it is not trivial to accumulate large amounts of user data in real-time healthcare applications to train powerful predictive models with high-quality training data. On the other hand, if the collection of user data is allowed, it is still not trivial to process these crowd-generated data, since the volume and velocity of the incoming data at the central server of HSP put a lot of burden on the network backhaul, delimited by the processing and storage capabilities of the central server.