# Federated Learning and Its Role in the Privacy Preservation of IoT Devices
### Roles of FL Applications  
The applications of FL have expanded to various fields, including security, telecommunications, the Internet of Things, and medicine. FL seeks to prepare an ML algorithm without directly sharing data samples, such as artificial neural networks, using various local data available in current situations. Figure 2 shows the development of FL between 2016 and 2022 (Google Trends) [8]. A common goal is to train local models in local data samples and perform a periodic exchange of bounds (such as deep neural network strengths and perceptions) among these local nodes to produce a universal model distributed by the whole device. The most significant difference between FL and distributed delivery is the supposition of local databases, as the distributed learning initiative aims to simulate IoT device performance. In contrast, FL aims to train on different databases. Although federated learning introduces a single model on multiple servers, it is common to assume that the local databases are evenly distributed and approximately equal in size. Instead, the datasets are usually different, and the sizes could be extensive instructions of implication [9]. The clients participating in federated learning may be inefficient because they deeply trust less-efficient communications and battery-powered IoT devices than clients participating in distributed data, where shared nodes interact with high computing power [10]. FL works like this: The client machine installs the current model, updates it with data from its device, and then encapsulates the improvements as a small, targeted change. This adjustment is only when the model is transferred to the server through secured  
Figure 1: Local vs. global models in FL. connections. It may easily be coupled with other device improvements to strengthen the sharing models [11]. The user's device maintains all training data, and no updates are saved on the cloud. The cloud service is utilized during the learning process to schedule FL, set different algorithms, and link all the participating nodes [12]. The service is responsible for selecting the connections starting the preparation stage and compiling the accepted model updates. The server can be a bottleneck as all selected nodes must submit updates to an association [13]. IoT devices can join a distributed learning environment to find a global model. Since model updates are exchanged between related devices without establishing a central server, this avoids single-point failure. However, the network topology can affect the learning process's efficiency. Various applications are used on smart and IoT devices [14]. Most current FL strategies assume local models share the same structure as the global model context. Heterogeneous FL (HeteroFL), a modern digital learning system, has recently been developed to meet the needs of a wide range of computer-enabled and highly connected customers. The HeteroFL method can produce a single tendency model while training multiple local models with a wide range of dynamic variables. Our study begins with exploring and discussing the various ML structures before reviewing the FL. Throughout this study, the authors offer a novel classification of FL themes and research fields based on a massive review of the innovative enabling issues and existing previous works [15], which differs from prior surveys in the area. A complete control system, in this sense, includes a wide variety of demanding features, contributions, and documentation trends, such as basic program models and projects, application domains, privacy and security, and resource management. The authors also go through some of the most pressing issues and current research indications [16]. The authors discuss key challenges and present research indicators for effective FL programs. People nowadays create massive volumes of information on networked machines such as mobile devices or IoT gadgets, portable health products, etc.