###### Abstract  
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL's inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature. Federated Learning (FL); intrusion detection systems (IDS); Internet of Vehicles (IoV); deep learning; machine learning +
Footnote †: journal: _Future Internet_  
cyberattacks, which threaten its stability, robustness, and can lead to vehicle unavailability and traffic accidents. Since communication in these networks requires the involvement of multiple components, they are susceptible to a broad array of attacks. Thus, ensuring their security requires advanced intrusion detection systems (IDSs) that can address potential cyberattacks. IDSs excel at identifying anomalies and attacks in the network's data during communications between vehicles and various devices. Given that the IoV is a relatively new network paradigm, new and ever-evolving attacks against it continue to emerge. The IoV network creates a huge amount of data very quickly, especially when there are cyberattacks.The accuracy of machine learning and deep learning approaches makes them a preferred choice in this high-stakes environment [1]. Nevertheless, the need to store and transmit data to a centralized server may compromise privacy and security. In contrast, Federated Learning (FL), a decentralized learning approach that protects privacy, trains models locally before sending only the parameters to the centralized server. Even though FL for IDS development has made significant progress, a comprehensive survey specifically exploring the applications of FL for IDS in the IoV environment has yet to be conducted. To the best of our knowledge, a gap exists in the availability of a study that comprehensively assesses current IDSs based on FL for IoV, similar to the successful systems research conducted in deep learning.