## 4 Discussion of Challenges and Future Research Directions
Future advancements in this field primarily focus on developing encryption techniques that successfully reconcile the requirements of security, efficiency, and the necessity for collaborative learning. Research efforts aim to establish standardized encryption protocols tailored specifically for IoV settings. These protocols are intended to ensure data security and integrity while facilitating seamless model updates and promoting collaborative learning within a broad spectrum of vehicular networks. * Edge computing: Incorporating edge computing into FL-enabled IDSs within IoV introduces both challenges and potential solutions. While local data processing on devices has the potential to alleviate network bandwidth demands, it also brings about issues related to resource limitations and data diversity. IoV devices, often constrained in terms of available resources, face difficulties when attempting to execute computationally intensive FL algorithms on the device itself. Furthermore, ensuring consistency and accuracy in updating models across various vehicles with different hardware configurations and data formats presents a significant challenge. Future research in this domain seeks to enhance the effectiveness of edge computing methodologies, facilitating efficient local data processing and collaborative learning while mitigating the variations in device capabilities. Leveraging edge computing, IDSs empowered by FL in IoV can realize benefits such as reduced communication overhead and improved real-time intrusion detection capabilities [68]. This, in turn, contributes to the establishment of more secure and responsive vehicular networks
* Optimization of Federated Learning and intrusion detection system parameters: FL predominantly relies on deep learning models that involve a diverse set of trainable parameters, which the user can configure. Additionally, IDSs are highly sensitive to these parameters. The next research avenue in FL-enabled IDSs for IoV involves optimizing FL and IDS parameters, as this directly impacts performance and training effectiveness [45]. Given the dynamic and diverse nature of IoV environments, it becomes imperative to identify the most suitable parameters for FL algorithms. This includes determining appropriate learning rates, aggregation methods, and local model parameters. In addition, customizing these parameters for specific intrusion detection tasks and diverse vehicular datasets can significantly improve the performance and accuracy of FL-enabled IDSs [51]. Future research should explore these factors in greater depth, utilizing methodologies such as hyperparameter tuning and adaptive learning algorithms [51]. By optimizing these parameters, researchers can finely tailor FL-enabled IDSs to suit specific IoV scenarios. This optimization process ensures effective collaboration, precise intrusion detection, and minimized communication overhead, ultimately paving the way for the development of more robust and responsive vehicular security systems. * Heterogeneity and interpretability of the Federated Learning model: In the realm of FL-enabled IDSs for IoV, the heterogeneity and interpretability of FL models are of paramount importance. Heterogeneity stems from the distinct characteristics of vehicular data and the varying capabilities of different vehicles and their sensors. Coordinating multiple models for effective collaboration, especially in real-time intrusion detection, introduces a high degree of complexity. Moreover, prioritizing the interpretability of these models is crucial, as it enables a comprehensive understanding of the rationale behind intrusion alerts. This understanding is valuable for both developers and end-users.