## 4 Discussion of Challenges and Future Research Directions
Future research endeavors are geared towards developing approaches that harmonize these diverse models, ensuring their seamless integration to enhance intrusion detection accuracy Simultaneously, researchers are dedicated to enhancing the interpretability of FL models through methodologies like explainable AI, which provides insights into the decision-making processes of these models. Byeffectively addressing these challenges, FL-enabled IDSs in IoV can achieve a state of equilibrium that encompasses various data sources, model interpretability, and efficient intrusion detection. This, in turn, fosters confidence and comprehension among stakeholders in vehicular security. * Big data management: Effective management of big data poses a significant challenge within the context of FL-enabled IDSs in IoV. The sheer volume, velocity, and diversity of data generated by vehicles require robust storage, processing, and analysis capabilities [69]. The integration of FL-enabled IDSs necessitates the use of extensive data for training and model updates. Efficiently handling this vast amount of data is paramount. The complexity lies in maintaining timely data collection, aggregation, and storage while preserving real-time intrusion detection capabilities, particularly when considering the limited resources of vehicle networks. Future studies will concentrate on creating distributed and scalable storage systems, better data processing algorithms, and advanced data analytics methods. By addressing big data management challenges, FL-enabled IDSs in IoV can harness the wealth of vehicular data efficiently, enhancing the precision and agility of IDSs in dynamic and networked vehicular environments. * Sparse data: Vehicle data, especially regarding specific types of security threats, can be sparse and unevenly distributed across vehicles. Data sparsity may lead to biased models, as they might not adequately capture certain types of intrusions. Consequently, this limitation can hinder the overall effectiveness of the IDS. Addressing the issue of sparse data requires innovative methodologies, such as data augmentation, imputation approaches, or customized algorithms designed to handle incomplete datasets effectively [70]. Future research efforts aim to develop algorithms that can successfully enable FL models to learn from limited and irregular data. By effectively tackling the issue of sparse data, FL-enabled IDSs in IoV can enhance their precision, ensuring a more comprehensive and nuanced understanding of various intrusion patterns across different vehicular scenarios. * Stability: Stability is a significant challenge within the context of FL-enabled IDSs in IoV. The inherent instability of the FL process is introduced by the dynamic nature of vehicular networks, characterized by the continuous changes in the composition and positions of vehicles. This variability can potentially disrupt the FL environment, affecting the consistency and accuracy of the IDS models. Maintaining stability requires the implementation of robust systems to address fluctuations in participation rates, network disconnections, and intermittent data availability [69]. Future research aims to develop algorithms that can adapt dynamically to changes in the network, ensuring the stability of FL models, even when confronted with evolving IoV scenarios. By addressing this challenge, FL-enabled IDSs in IoV can consistently perform at a high level, providing reliable capabilities for detecting unauthorized access despite the everchanging characteristics of vehicular networks. * Reliability: Applications related to intelligent transportation and unmanned aerial vehicle detection demand a high level of reliability due to their safety-critical nature. Failures in meeting reliability standards can lead to severe consequences, including significant loss of life and property. Achieving reliability in the context of intrusion detection within a diverse and dynamic vehicle network presents significant challenges.