## 1 Introduction  
A machine learning (ML) model for a specific task is created utilizing the unprecedented amount of data that are generated from devices. These data can be used in various tasks, such as achieving process optimization [1], gaining insight discovery [2], and decision making [3]. Some examples of devices that generate data can be smartphones and wearable devices, smart homes, etc. Traditionally, to implement ML predictive models, the data would need to be transferred to a central server where it could be stored and used for training and testing ML models designed for specific tasks [4]. However, the imposition of privacy and data-sharing policies, such as global data protection regulations (GDPR), caused various restrictions on using data centrally [5]. As a result, the traditional centralized method of transferring data to the server could present more challenges. In addition to this, other computational issues are present with this traditional approach, and it is critical to deploy an alternative solution for creating reliable ML models within various tasks [4]. Federated learning (FL) is an innovative way to implement ML algorithms in a decentralized setting, and was introduced by a research team at Google for the first time [6]. FL attempts to answer the question: Can machine learning models be trained without needing to transfer user data onto a central server? [7]. FL aims to propose a more collaborative approach to ML while preserving user privacy by training the data in a decentralized environment rather than a central server [8]. This collaborative learning method can be explained by using data generated from different participants, such as hospitals, research centers, or even mobile end-users, where the participants actively collaborate in the training procedure. For example, different hospitals generate data from their smart devices and/or equipment. Using these data can be utilized to create an ML model for a specific task. However, due to data-sharing policies, such as the health insurance portability and accountability act (HIPAA) and GDPR, it would not be possible to access all data in one place. A single hospital may not also generate enough data for ML training tasks, possibly leading to a biased or unreliable ML model [9]. This is where FL and its collaborative nature can thrive. Each hospital can train its own independent ML model that gets uploaded to a central server where all models average out as a global ML model for the assigned task. Figure 1 demonstrates an application of FL for a healthcare system where hospitals participate in training a global model as discussed above. In general, computational power, training time, and, most importantly, security and privacy are different challenges that the traditional centralized-based ML approaches deal with. FL provides an effective way to preserve user security and privacy concerns by decentralizing data from the central server to end-devices, enabling AI benefits to all FL participants. In fact, rather than relying on the centralized server for training the ML model, in FL, the training process takes place on end-devices directly. Although we still have the concept of a central server in FL, the training procedure is mainly carried out by end-devices locally in order to enhance security and privacy concerns associated with transferring data. In addition, in an FL environment, the computational power is also split amongst parties within the federated network using the decentralized algorithms. FL is considered as one of the growing ML fields in recent years, where its security and privacy features are perfectly aligned with user data protection laws [10]. These security and privacy-preserving promises of FL technology have attracted much attention from different data-sensitive domains, such as healthcare [11], where the sensitive data are stored locally but, at the same time, the data are used in the training process.