## 3 Related Work  
The contemporary machine learning research field is faced with two major challenges: data isolation and privacy and security issues. In methods utilizing standard ML, centralized training data are necessary. Around the world, laws are implemented to protect the privacy of data [6; 7; 8]. Therefore, the main difficulty for conventional machine learning algorithms is data privacy. A federated stochastic gradient descent (FedSGD) and federated averaging (FedAvg)-based technique first developed by Google in [9] offered some hope for overcoming these difficulties. In [10], a method based on FL was suggested to address the problems of data silos and privacy. To address the problems with data security in the conventional artificial intelligence field, they created an extensive architecture based on federated learning. Their suggested solution was divided into two categories: horizontal and vertical FL. A description of the different machine learning deployment models, including centralized, distributed, and federated learning, was given by the authors in [20]. With careful consideration, they have described how machine learning architectures have developed. The authors of the research in [21] created a federated learning-based model for individuals with diseases that are likely to require hospitalization. They made use of data from electronic health records (EHRs) spread across various sources or agents. To use FL to solve the issue of large-scale sparse computing, the authors presented the clustering-based approach for dual splitting. Their suggested method yielded similar classifier prediction accuracy. The MNIST dataset was utilized by the authors of [22] to test and assess the three FL-based methods. A Bayesian correlated \(t\)-test was also employed. When client uploads were restricted to 10,000, FedAvg surpassed CO-OP and FSVRG algorithms, in their assessment. They have employed balanced data distribution, where each customer receives the same volume of information. The authors of [23] suggested a modified version of the standard FL with the aim of improving the algorithm's accuracy and convergence rate. To implement Bayesian non-parameterized approaches for heterogeneous data, they introduced the FedMA algorithm, which is a layer-wise version of the FL algorithm. Their suggested FedMA outperformed in terms of convergence, accuracy, and communication size reduction. The authors of [24] examined technical issues and other factors regarding the data privacy in the distributed implementation environment for FL algorithms. In their study, they outlined the features and results of a few of the optimization strategies for FL implementation. Additionally, they have discussed certain commercial consequences for federated learning that will be expected. The authors of [25] have suggested an algorithm that distributes weights according to how much each class contributes to the local models. Using patients' chest x-ray data, machine learning-based algorithms can contribute to the identification of COVID-19. In contrast to conventional machine learning techniques, an FL version was suggested in [26] to discover COVID-19 with improved prediction accuracy. A blockchain-based approach based on federated learning was suggested by authors in [27] to address the problem of data privacy for IoMT-based healthcare systems. Their suggested solution was a hybrid strategy built on federated learning and the maximum approximation of the Gaussian mixture model, and it used blockchain to address the issue of user data privacy. Their suggested approach demonstrates that IoMT data training may be carried out utilizing local privacy to stop data leaking. In the past, researchers gathered sensed data from HSP devices and then utilized that data to predict about several diseases. The authors of [28] suggested a version of FL with a Bayesian inference model to construct a privacy-aware heart rate prediction approach.