## 2 Literature Review
A Federated learning-based technique is proposed by the authors in [26] to detect COVID-19 cases with improved model prediction accuracy and loss as compared with the traditional machine learning algorithms. For their work, the authors utilized two datasets which are descriptive datasets with COVID-19-infected cases from Wuhan and patients' chest x-ray radiography images with COVID-19, Pneumonia, and normal images. To resolve the issue of data privacy for the IoMT-based healthcare system, authors in [27] proposed a blockchain-based solution using federated learning. Their proposed algorithm is a hybrid approach based on federated learning and maximization of the Gaussian Mixture Model (FL-EM-GMM) and uses blockchain for model verification, and homomorphic encryption to overcome user data privacy issues. Their proposed method shows that the IoMT data training can be completed using privacy locally to prevent data leakage. Traditionally, the cloud/server collects sensed data from IoMT devices and then performs the prediction of that sensed data. To develop a privacy-aware heart rate prediction technique, authors in [28] proposed a Bayesian inference federated learning with autoregression with exogenous variable (ARX) model. This FedARX method accomplishes accurate and robust heart rate prediction as compared with the traditional machine learning models. To effectively manage and optimize the computation offloading for IoT-based applications, authors in [29] proposed a meta-heuristic Artificial Bee Colony (ABC) optimization. Their technique intelligently manages the computation workload for resource-constrained IoT applications. Authors in [30] proposed the ABC algorithm for the optimization of numerical problems in a computing environment. For lightweight prediction of computational workload in an IoT-assisted Edge environment, authors in [31] proposed an artificial neural network-based framework. Their proposed multi-objective framework enhances workload management for computationally intensive applications. A long short-term memory (LSTM) based prediction of computational workload technique for offloading in IoT-assisted Mobile Edge Computing is proposed in [32]. A detailed survey of intelligent offloading of computational workload is prepared by authors in [33]. An extensive survey of open-source datasets for the COVID-19 disease is performed by authors in [34]. They categorized the datasets into four classes as the identification of COVID-19 from X-ray images, CT scans, and cough sounds, as well as transmission estimation, case reporting, and diagnosis from demographic, epidemiological, and mobility data. Other methods were also introduced in the literature for heart disease prediction, such as a hybrid approach of linear discriminant analysis with the modified ant lion optimization for classification [35], a combination of Fuzzy logic algorithm and gradient boosting decision tree (GBDT) [36], a technique based on modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) [37], and multi-cost objective function [38]. Heart disease monitoring and prediction based on a hybrid classifier and deep learning centered modified neural network for IoT-assisted healthcare is proposed in [39; 40; 41; 42]. Moreover, various methods are proposed for improving the classification error and accuracy, such as the higher-order Boltzmann-based model [43], performance evaluation of classifiers and optimizers for heart disease prediction [44], localization using two-stage classifiers [45], a hybrid classifier based on random forest and naive bayes [46], hybrid recommender system [47], based on genetic algorithm and hybrid classifiers using the ensembled model with a majority voting technique [48], and Artificial intelligence (AI) based heart disease detection using electrocardiogram (ECG) signals [49].