## 2 Literature Review
More  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  
formally, it is believed that this idea originated at Google within the scope of the Federated Learning Framework; it has its advantages, which include the feasibility of shifting some computational power to the edge of the network, where data do not leave the device [26]. Their efforts laid the foundation for the adoption of FL in subsequent domains, including the IoV. In Anomaly Detection in In-Vehicle Networks, the authors proposed a self-supervised anomaly detection for the in-vehicle networks with the help of federated learning. This work illustrates how FL can be an insufficient resource efficiency achievement without violating vehicle data privacy [27]. With the rapid increase in the number of devices that can be connected to a network, and the threats that this poses, security for IoT and IIoT systems has never been more critical. Intrusion detection has been the most researched area in such environments and many datasets and methods have been developed to this effect [28]. These days, however, one of the most favorable lines of approach is federated learning as data are not transmitted, thus ensuring privacy and security. The paper has presented Edge-IIoTset which is a novel dataset about cyber security of IoT and IIoT applications. This dataset can be utilized not only in training and assessing the models through Classical Centralized Learning but also in training and assessing in a Federated Learning environment. It was created using a special IoT/IIoT testbed comprising devices, sensors, protocols, and clouds/edges. It contains 61 derived attributes extracted from the set of 1176 origin attributes, which proves to be useful in the construction of a machine learning-based Intrusion Detection System (IDS) [29]. This dataset was evaluated using different machine learning algorithms, including Decision Tree, Random Forest, Support vector Machine, K-Nearest Neighbor, and Deep Neural Network. Another limitation is related to the scope of the Edge-IIoTset dataset and, as the authors mentioned, when applying big data solutions, computational requirements can be very high. However, due to its extensive planning scope and the integration of federated learning, it is recommended to use it for the development and testing of IoT/IIoT security solutions [30]. The Federated TON_IoT Dataset is the second major contribution that is the focus of the paper under review. Within the UNSW Canberra's IoT Research Lab, a dataset has been created to evaluate the security of IoT/IIoT systems through federated learning. The dataset reflection includes IoT-services telemetry, operating systems, and network traffic as an example and comprises nine attack types, including DoS/DDoS, scanning, ransomware, backdoor, injection, and Cross. About the features, there are fifty of them, and they were selected since they were deemed to be dependent on one another. This database is wide enough, but it seems that the evaluation of intrusions is not quite exhaustive by utilizing various machine learning techniques to support [31; 32] this dataset. However, the Federated TON_IoT dataset is still more useful in the development of federated learning for the mentioned approach within the context of IoT/IIoT systems [33]. The present work has suggested a system to detect the spoofing of location information in the IoV through an efficient Ens. RF classifier. This was performed on the VeReMi dataset, which has four types of location falsification attacks and benign traffic. Features were lowered using Pearson's Correlation Coefficient to the available number of features.