## 4 Discussion of Challenges and Future Research Directions
Preserving security in FL-enabled IDSs within IoV scenarios is of utmost importance for protecting against a diverse range of potential cyberattacks and maintaining the efficiency of IDSs in interconnected vehicular networks. * Privacy concerns: Privacy considerations emerge as a significant challenge in the context of FL-enabled IDSs in IoV scenarios. Although the primary purpose of FL is to address the privacy concerns associated with centralized learning methods, FL may still inadvertently disclose information from the training data of individual clients. FL relies on data provided by individual vehicles for the purpose of training models, raising issues concerning user privacy and data confidentiality. Within IoV, vehicles can generate substantial quantities of sensitive data, including location information, driving behavior, and recordings of communication. The central issue revolves around the need to effectively utilize this data for training IDS models while safeguarding the privacy of both vehicle owners and occupants. As a result, there has been a notable surge of interest has occurred in implementing privacy-preserving methodologies in the field of FL [23]. These methodologies include differential privacy techniques, SMPC, and homomorphic encryption. However, using these advanced approaches often entails a trade-off in terms of precision and effectiveness, potentially compromising the IDS's ability to identify attacks. Deploying these advanced methods is necessary to strike a balance between the need for effective intrusion detection, strict privacy requirements, and meeting user expectations. Further research is required to find the optimal balance between privacy and performance to develop efficient IDS methodologies. * Communication efficiency: Implementing FL-enabled IDSs within IoV introduces a significant challenge in terms of communication efficiency. In IoV scenarios, where vehicles are in constant motion, transmitting substantial amounts of data to train FL models on a central server can strain network bandwidth and result in significant communication overhead. This challenge is further exacerbated by the need for real-time intrusion detection, where rapid responses are crucial. Optimizing communication protocols and data transmission techniques is essential to alleviate the network's burden while ensuring the timely delivery of relevant data to the central server for model updates. Future research in this field is oriented towards developing sophisticated communication-efficient techniques tailored specifically for IoV scenarios. Approaches such as model quantization, edge computing, and strategic data sampling can be leveraged to minimize the volume of transferred data, thereby enhancing communication efficiency. Balancing the requirement for extensive data exchange with the constraints imposed by network bandwidth is essential for the effective implementation of FL-enabled IDSs in dynamic and bandwidth-limited IoV environments. Research efforts also focus on exploring 5G and beyond-5G technologies, which hold the potential to provide increased bandwidth and reduced latency. These advancements can significantly transform the communication landscape of FL-enabled IDSs in IoV. * Encryption standards: Encryption standards play a significant and multifaceted role in the context of FL-enabled IDSs within IoV. Ensuring the security and privacy of sensitive vehicular data during the transmission process is of paramount importance. The main challenge lies in adopting encryption standards that combine robustness and efficiency to effectively manage the substantial volumes of data transmittedbetween vehicles and central servers. Moreover, within the FL framework, which entails collaborative model training on various devices, selecting encryption methods that can protect data while preserving the integrity of the collaborative learning process is a complex task [67].