## 4 Discussion of Challenges and Future Research Directions
These constraints must be acknowledged and addressed to create resilient IDSs that effectively capture the complicated nature of actual IoV settings while upholding user privacy and data integrity. * Aggregator as a bottleneck: In the context of IoV scenarios involving FL-enabled IDS, the aggregator frequently becomes a bottleneck despite being a central component. The processing capacity of the aggregator can be overwhelmed by the sheer volume of incoming information if data from multiple vehicles are sent to the aggregator for model training and updating [65]. The influx of data, especially in extensive IoV networks, has the potential to result in delays when it comes to aggregating and updating FL models. Furthermore, given the real-time nature of intrusion detection in vehicle contexts, introducing any delay at the aggregator level can impede prompt responses to security threats. The challenge of balancing the requirement for comprehensive model updates with the practical constraints of aggregators is of utmost importance. This necessitates using innovative approaches in distributed computing, efficient algorithms, and optimized communication protocols. These measures are necessary to address the bottleneck and ensure the smooth operation of FL-enabled IDSs in IoV scenarios. * Client selection: Identifying suitable clients for FL-enabled IDSs in the IoV context presents a significant challenge. During each training iteration, the coordinator can choose a specific subset of devices to engage as FL clients in the training procedure. The environments in which IoV operates exhibit a high degree of dynamism, characterized by the continuous movement of vehicles within and beyond the network coverage area. The dynamic nature of the environment poses difficulties in maintaining a consistent group of clients who actively participate in the training of FL models. For instance, specific devices may not be accessible during a particular round due to mobility issues or disruptions in connectivity. In addition, the criteria for selection need to consider factors such as the device's current state, its battery life, its computational and networking capabilities, and even the precision of the ML technique. The client selection process can significantly impact the accuracy achieved and, consequently, the detection of potential security breaches within the framework of an IDS approach. Striking a balance in the client selection process, where a diverse, accurate, and current dataset is maintained, necessitates the utilization of advanced algorithms and real-time decision-making to manage the ever-changing pool of participating vehicles effectively. Addressing this challenge is essential to maintain the integrity and accuracy of FL-enabled IDSs in IoV scenarios. Therefore, future strategies for devising an efficient client selection process in IoV systems must consider the dynamic nature of device conditions throughout each training iteration. * Security attacks: In the context of FL-enabled IDSs in IoV scenarios, security attacks pose a severe threat. Attackers can exploit vulnerabilities inherent in the FL architecture [66]. These exploits can manifest as various types of attacks, including data poisoning [24], where adversaries inject deceptive data into the training process to manipulate the IDS model [45]. Model inversion attacks can also occur, in which attackers attempt to deduce confidential data from the trained model. In addition, the confidentiality and integrity of data might be compromised by eavesdropping attacks that specifically target the communication channels established between vehicles and the central server. To address these security concerns, robust security measures are essential, including strong encryption, secure communication protocols, anomaly detection techniques, and continuous monitoring.