## 3 Background
### Federated Learning  
FL has been considered in critical networks to handle the centralized data problems (e.g., VANET) as a solution. It is a sort of distributed ML model with benefits of AI-based end-devices computation applications, such as smart cars, and also guarantees the privacy of end-users [69; 70]. The ML/DL model is trained globally and distributes the updated parameters to end devices' via the centralized server to start the process in FL. With the assistance of this trained model, all end devices train their local model of ML/DL on their local data. Clients send the parameters to a centralized server for global training upon training the local ML model. To fulfill the demand and precision of centralized servers, the operation is repeated multiple times until it is all done. There are several applications, such as e-commerce and e-healthcare, where FL can be conveniently deployed [69; 71], as is shown in Figure 4.  
In ML, the accuracy and efficiency of trained models rely on the central server's training data and processing resources (Computational Power). Throughout the conventional ML approach, centrally stored data are utilized for both training and testing purposes to construct efficient and comprehensive models. Several challenges are associated with centralized ML algorithms concerning user data, including time, computational power, privacy, and security. To solve the described concerns as a technology solution, FL has recently emerged [70]. The FL approach incorporates statistical model training over data centers or remote devices, e.g., hospitals and mobile phones, keeping user data localized. Statistical model training in potentially significant and heterogeneous networks provides a new research challenge that demands standard approaches for large-scale distributed optimization, ML, and privacy of acquired data analysis [72; 73].  
Wearable gadgets, mobile phones, and driverless cars are a few examples of distributed networks that produce a tremendous amount of data each day. Because of the increased  
Figure 4: Usage of federated learning in a VANET environment.  
processing capability of the devices indicated above, the transmission of private data is a key concern. FL enables privacy to user data by decentralizing data to end devices from central servers and facilitates artificial intelligence (AI) benefits to heterogeneity and sensitive data fields. The FL model is used for two motives: the inaccessibility of adequate data stored on the central server because of the restrictions to data and the protection of private data through edge devices using local data, e.g., clients [19].  
Keeping user data privacy delivers probability to influence AI advantages using ML/DL-trained models effectively in different domains. Furthermore, the computational time and power are divided into associated parties instead of the central server by training the iterative model on the end device. Because of the decentralized concept, in the area of ML/DL, FL is the growing field in recent years because of its privacy and security features regarding user data protection [74; 75]. Besides privacy, FL also allows ML and DL advantages to other domains where enough data are not accessible for training to create a separate trained ML/DL model. Figure 4 presents the use of federated learning in a VANET environment.