## 2 Literature Review  
Privacy and security of data, and data in an isolated form are the two big challenges faced by the current machine learning research domain. Techniques based on machine learning require centralized training data for the model to be trained. Regulations are put into practice for data privacy throughout the world [6; 7; 8]. Hence data privacy is a big challenge for traditional machine learning techniques. Federated learning initially proposed by Google, federated stochastic gradient (FedSGD), and averaging (FedAvg) based algorithm brought a ray of hope to overcome these challenges [9]. A technique constructed on federated learning is proposed in [10] to overcome the issue of data isolation and privacy. They proposed a comprehensive framework based on federated learning to tackle the issues related to data security in the traditional artificial intelligence domain. Their proposed solution is categorized into two approaches i.e., horizontal, and vertical federated learning. Technical aspects such as hardware, platforms, software, protocol, enabling technologies, and other features of the data privacy of federated learning are discussed by the authors in [19]. The authors discussed some of the optimization techniques for federated learning in their article by highlighting their features and performance. They also outlined some of the market implications of federated learning in order to anticipate them. Additionally, some of the advantages, issues, and challenges which refer to the design and deployment of federated learning are presented by the authors. In [20], the authors provide insight into the various machine learning deployment architectures such as centralized, distributed, and federated learning. They have outlined the evolution of machine learning architectures with comprehensive deliberation. Moreover, application areas for federated learning such as the IoT systems, healthcare, Gboard App, edge computing, cybersecurity, and many others were suggested by them. In the paper [21], the authors developed a model based on federated learning for the prediction of hospitalization of health-related disease patients. They used electronic health records (EHR) data distributed amongst numerous sources or agents. The authors proposed the cluster Primal Dual Splitting (cPDS) algorithm to overcome the problem of large-scale sparse Support Vector Machine (sSVM) using a federated learning technique. Their proposed technique achieves analogous prediction accuracy of the classifier. Authors in [22], tested and evaluated the three federated learning-based algorithms on the MNIST dataset and used a Bayesian correlated t-test. According to their evaluation, FedAvg outperforms CO-OP and FSVRG algorithms when the uploads by clients are limited to 10,000. They have used balanced data distribution in which the clients have the same amount of data. An optimized version of FedAvg is proposed by authors in [23], in which they intend to enhance the accuracy and convergence rate of the state-of-the-art federated learning algorithm. They proposed the Federated Match Averaging (FedMA) algorithm based on the layer-wise federated learning algorithm to adopt Bayesian nonparameterized methods for heterogeneous data. Their proposed FedMA performs better than FedAvg in terms of convergence, and accuracy, and reduces the communication size. To optimize the convergence speed of federated learning, the authors in [24] proposed a fast-convergent algorithm that achieves intelligent selection of each device at every round of the training model. Their algorithm utilizes precise and effective approximation for communication of a near-optimal distribution of device selection to improve the convergence rate. Authors in [25] have proposed an algorithm that assigns the weights according to the contribution of each class to the local models. The machine learning based algorithms can play their part in the detection of COVID-19 using a dataset of chest X-rays of the patients.