## 1 Introduction  
Advancement in technologies like the Internet of Things (IoT) and wearable sensing devices enables the storage of records related to the health parameters of patients or people. The IoT in the healthcare environment has led to a new research domain of the Internet of Medical Things (IoMT). The IoMT-based solutions integrated with the healthcare system can enhance care services, and quality of life, and enable cost-effective solutions [1]. Biomedical data related to people like medical records, images, physiological signals, and many other forms are gathered using these technologies. The volume of this biomedical data is huge as it can easily be gathered from a huge number of people using modern technologies [2]. Wearable sensing devices, like smartwatches, wristbands, and many others, enable early detection and warnings of several diseases. The increasing trend in wearable devices helps in efficient data collection and the early detection of diseases. Healthcare is a system that is formed with the intention to prevent, diagnose, and treat various health-related problems in humans. As the advancement and development of healthcare-related technologies take place, data in huge amounts is available from various sources. The development of an efficient healthcare infrastructure system is one of the challenging goals of current modern society. One of the primary health concerns faced worldwide is cardiovascular disease. According to the World Health Organization (WHO), approximately 18 million deaths occur yearly worldwide due to heart or cardiovascular disease [3]. Heart disease or cardiovascular disease (CVD) is based on various conditions that impact the human heart. Many factors cause heart disease including personal and functional behavior and genetic predisposition. Numerous risk factors include smoking, excessive consumption of caffeine, and alcohol, inactivity, stress, and physical fitness, as high blood pressure, obesity, pre-existing heart disease, and high cholesterol can also be a reason for heart disease. CVD is a serious condition that affects the function of the heart and causes problems such as strokes and reduced blood vessel function. Patients with heart disease do not reach the advanced stages of the disease and it is too late for the damage to be repaired. Early and accurate treatment of heart disease plays a significant role in avoiding death. Machine learning (ML)-based techniques provide a way forward for effective diagnosis of heart disease. A lot of research has been performed and various machine learning models have been used to make classifications and predictions for diagnosing heart disease. A hybrid technique based on random forest and a linear model is suggested in [4] to improve the prediction accuracy of heart disease. For the identification of heart disease in the E-healthcare system and to resolve the problem of feature selection, a system is proposed in [5] based on classification algorithms. Machine learning (ML) models are frequently trained on sufficient user data in healthcare to track a patient's health status. Regrettably, today's healthcare faces two critical challenges. For starters, real data is frequently found as isolated islands. Even though there is a large amount of data in various organizations, sharing this data is impossible due to concerns about privacy and security. As a result, training powerful models with valuable data is difficult. In addition, the European Union through General Data Protection Regulation (GDPR) [6], China by China through China Cyber Security Law [7], and the United States with the California Consumer Privacy Act (CCPA) of 2018 [8], have recently enforced the protection of user data privacy through these regulatory procedures. Therefore, it is not possible to get huge amounts of user data in real-time healthcare applications. To overcome these challenges, federated learning is proposed recently by Google [9; 10].