## 3 State of the Art
### Related Surveys  
A few reviews have focused on the topic of FL-based IDSs. Table 8 succinctly outlines the primary differentiators between our work and the previously conducted surveys. For instance, ref. [45] offers a comprehensive survey of FL-based IDS approaches and discusses the difficulties and challenges of using these methods. This review also outlines potential future directions for FL in IDS. Meanwhile, the authors of [27] focus on the current scientific progress of FL applications in attack detection problems for IoT and explore these applications. The extensive review presented in [50] draws from an analysis of 39 research papers published from 2018 to March 2022, with a specific focus on the IoT. The analysis examined evaluation variables related to IoT, particularly concerning FL, and identified and dis-cussed prospects and unresolved issues pertaining to FL-based IoT. The authors of [25] also provided an overview and comparison of six studies that use FL to enhance IDS effectiveness for IoT. In the absence of specific datasets for assessing FL, the authors emphasized data partitioning modeling among clients. Additionally, they investigated the modeling of bias in the test data to assess its impact on the effectiveness of the ML model. The authors of [51] discussed the implementation of FL-based IDSs in various domains and highlighted distinctions between different architectural configurations. Their structured literature analysis offers a reference architecture that can be used as a set of principles for comparing and designing FL-based IDS. Despite significant progress in FL for IDS development, a comprehensive survey exploring FL for IDS applications within the context of IoV is conspicuously lacking. To the best of our knowledge, no survey has thoroughly evaluated existing IDSs based on FL for IoV. In this direction, we present an organized literature analysis that examines recent developments in IDSs based on FL in an IoV environment. The review covers the years from 2016 (when FL was first introduced) to 2023. We conducted our search using the terms \"federated learning\", \"intrusion detection\", and \"internet of vehicles\". Comparative Analysis of Federated Learning-Based Intrusion Detection Systems for Internet of Vehicles  
In the rapidly evolving landscape of cybersecurity within IoV, FL has emerged as a transformative paradigm, promising enhanced security and privacy preservation. As the IoV ecosystem expands, robust IDSs become essential to safeguard vehicles, passengers, and the underlying network infrastructure from ever-evolving cyber threats. This section offers an extensive analysis of the relevant literature in the field of IDS based on FL, specifically tailored to the intricacies of IoV. This comparative survey aims to extract significant insights by examining the unique techniques, strategies, and structures of recent studies. These insights are crucial for understanding the current state of IDS solutions based on FL in IoV and provide valuable guidance for future research. We employed a range of criteria to evaluate and differentiate the related works in the domain of FL-based IDSs within the framework of IoV. We formulated the following research questions for our review: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} \(\check{\surd}\) \\ \end{tabular} \\ \hline \hline \end{tabular}
**Note:** In this table, ✓: indicates that the survey discussed the relevant aspect of Federated Learning (FL) or Intrusion Detection Systems (IDS), while X signifies that the aspect was not discussed in the survey.  
\end{table} 
Table 8: Summary of related surveys on Federated Learning-based IDS. * What kinds of FL designs are used for IDS? * What ML model architectures are employed in the proposed solutions? * Which datasets are utilized for evaluating the proposed solutions? * What types of attacks can be identified by the proposed solutions? * Which measures do the authors employ to validate their proposed solutions?