## 4 Discussion of Challenges and Future Research Directions
IoV is to facilitate the integration of data and resources from a vast array of vehicles, users, infrastructure, and networks. This integration aims to enhance the reliability, connectivity, and ease of management, control, and operation of IoV systems. Nevertheless, this novel methodology also presents a series of challenges that necessitate meticulous consideration to guarantee the efficiency and security of these systems. These constraints arise from the heterogeneous characteristics of vehicle data, constrained network resources, the persistent risk of adversarial assaults, strict regulatory obligations, and the imperative to uphold the security of FL models. The ability to effectively deal with these intricacies is crucial to fully harness the capabilities of FL-enabled IDSs in IoV scenarios. This will establish a resilient, secure, and privacy-conscious vehicular network. Drawing upon the literature analysis, this section aims to elucidate some of the main challenges we found and possible future research directions for investigating the development of IDSs empowered by FL within the IoV context. Figure 3 summarizes the challenges and future research directions in FL-enabled IDS IoV. Here, we cover some of the primary challenges and future research directions associated with developing FL-enabled IDSs in IoV scenarios. * The deployment of Federated Learning on Internet of Vehicles devices: Deploying an FL-enabled IDS architecture on real IoV devices presents many challenges. One notable obstacle involves the presence of resource constraints since IoV devices frequently have restricted processing capabilities and memory capacities, making the efficient execution of intricate FL algorithms difficult. This challenge can be exacerbated when employing deep learning techniques, as they often require more computational resources than traditional ML [65]. To overcome these restrictions, a prevailing approach involves the implementation of intermediate nodes positioned at the network edge. These nodes serve as clients for FL, receiving data from end devices. Real-time processing poses an additional challenge in the context of IDSs in IoV. These IDSs need to effectively evaluate incoming data and promptly identify any instances of intrusion, requiring the implementation of algorithms that strike a delicate balance between accuracy and processing speed. Consequently, more work is needed to examine the real-world constraints of FL-enabled IDS techniques in IoV contexts to ensure optimal levels of security and efficiency. * Limitations of existing FL-enabled IDS datasets for IoV: The current datasets available for FL-enabled IDSs in the context of IoV exhibit various limitations. The issue of  
Figure 3: Challenges and future research directions in FL-enabled IDS for IoV. data diversity presents a notable obstacle as datasets may lack a comprehensive representation of the wide range of real-world scenarios and driving conditions, resulting in the development of biased models. Data imbalance is a significant issue that warrants attention, as specific categories of security threats may be inadequately represented in the dataset, posing challenges for the FL-enabled IDS to detect these less frequent intrusions accurately and efficiently. Data quality is essential, as any inaccuracies or noise present in the data can significantly impact the learning process, potentially leading to the development of intrusion detection models that are less reliable and potentially misleading. Furthermore, the issue of data privacy poses a significant constraint in the context of IoV. The data generated by IoV systems frequently encompass confidential personal and vehicular details, thereby presenting a formidable obstacle in creating extensive datasets that simultaneously safeguard users' privacy. The concern regarding the scalability of current datasets becomes particularly significant as IoV networks experience rapid expansion.