## 5 Applications of Integrating Federated Learning and Blockchain in Vehicular Networks
### Federated Learning and Blockchain for Privacy Preservation in Vehicular Networks  
Shortcomings of VANET are privacy, availability, integrity, identification, and confidentiality prevention from incoming attack [137; 138]. Authentication of each vehicle in a network is a key security feature that must be ensured while spreading data within or across the network. Previously, the identification system was based on Public Key Infrastructure (PKI), where each vehicle in a system exchanges its private encrypted identification message to the Local Authentication Center (LAC), which takes enough time to identify a single vehicle. Periodic encryption and decryption create overhead problems in a network, which in return affect the efficiency and reliability of a network [139]. Privacy assurance is becoming the primary concern as technology is intervening in our daily life [140]. Due to the limitation of mobility and resources of vehicles in VANET, there are two main problems with deploying special data privacy system [141; 142]. To ensure vehicles' data privacy and reduce latency, FL enables several entities with fewer resources, e.g., RDUs and vehicles, to combine and train a general model using local data of devices. During the data transformation process, to preserve data privacy, the raw data of the network is distorted by plotting this in different models with less sensitive information [143]. Leveraging FL, the integration of two different components can mitigate data privacy problems in VANET [144], as can be seen in Figure 5.  
To address the privacy issues in VANET, various researchers tried to solve the issues from various research angles. Existing privacy frameworks: the differential privacy framework [145], its extensions [146], and the classic privacy framework are not sufficient to solve the privacy issues in VANET. To date, researchers did not find an optimal global solution to the privacy issues in VANET. To date, researchers \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Limitation of FL** & **Solution Provided by Blockchain-Enabled FL** \\ \hline FL is not suitable for the aggregating updates while selecting vehicles and maintain GM. & \\ \hline High speed is required for the server to gather information and update vehicles (clients). & Blockchain provides a solution to all these problems through its decentralized storage and further maintaining the FL model. \\ \hline Skewing in GM can also be expected because of biasness. & Blockchain can be used to store GM. \\ \hline FL cannot detect the internal attacks by malicious node while updates are gathered from every vehicle in a network causing GM unable to link up. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Blockchain-enabled federated learning. tion for data privacy, and utility protection [147]. Ref. [148] proposed a encryption-based technique to solve the privacy issues. The proposed technique is helpful to a satisfactory level, but the proposed technique does not help in big data situations. FL can accomplish effective communication by transferring updated data between global models and local models [149; 150]. FL solves the privacy issues to the maximum level, but another issue can arise: if the central model is poisoned or compromised, the adversaries may launch successful attacks. To solve the trust issues in FL, blockchain technology is introduced in such situations [151]. By forcefully incorporating privacy solutions through blockchain, it will decrease the efficiency of the application. FL overcame several data privacy issues by dividing the data into two parts: global aggregation and local training in the learning phase, but several other security issues arose. Table 4 presents the literature review of security, privacy, and energy efficiency in the VANET environment.