## 4 Discussion of Challenges and Future Research Directions  
IoV is anticipated to experience significant growth in the coming decade, emerging as a prominent paradigm movement. This projection suggests that IoV will receive substantial attention and witness considerable advancements across several sectors and industries. The integration of FL into IDSs within IoV scenarios presents a significant opportunity to bolster the security of interconnected vehicles in this dynamic environment. The primary objective of incorporating collaborative intelligence concepts and technologies into the domain of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref** & **Advantages** & **Drawbacks** \\ \hline \multirow{3}{*}{[56]} & Three-level model aggregation. & \multirow{3}{*}{Complex system architecture.} \\  & Fuzzy Logic-Based FL Vehicle Selection (FLBFLVS). & & \\  & Reduced latency. & & \\ \hline \multirow{3}{*}{[52]} & Reduced model size and convergence time. High & \multirow{3}{*}{Complexity of implementation.} \\  & detection accuracy (over 95\%) & & Scalability concerns. \\ \hline \multirow{3}{*}{[53]} & Privacy preservation. Reduction in communication & \multirow{3}{*}{Complexity in implementation. Challenges in} \\  & overhead. Handling position falsification attacks. & & \\ \hline \multirow{3}{*}{[54]} & Trust estimator integration. Effective learning with fewer & \multirow{3}{*}{Complexity in implementation and model aggregation. Need for regular updates} \\  & rounds. Improved network performance & & and maintenance. \\ \hline \multirow{3}{*}{[55]} & Blockchain integration for trust. Smart contract use. & Resource intensity. \\  & Efficient consensus protocol. & Challenges in worker selection and bias. \\  & High performance. & Scalability in real-world deployment. \\ \hline \multirow{3}{*}{[16]} & High accuracy in cyberattack detection. & \multirow{3}{*}{Complex implementation.} \\  & Reduced communication overhead. & & \\  & Resource efficiency. & & \\ \hline \multirow{3}{*}{[57]} & Blockchain integration. & \multirow{3}{*}{Complex system architecture.} \\  & Decomposition using Fourier transform. & Resource intensiveness. \\  & High performance. & Challenges in blockchain integration. \\ \hline \multirow{3}{*}{[58]} & High accuracy (up to 98.45\%). & \multirow{3}{*}{Dependency on local data quality.} \\  & Low-complexity structure. & & \\ \hline \multirow{3}{*}{[59]} & High detection accuracy (beyond 90\%). & - \\ \hline \multirow{3}{*}{[60]} & High performance metrics (with an average 99.54\% & \multirow{3}{*}{\multirow{3}{*}{-}} \\  & F1-score and 99.87\% accuracy, alongside low & \multirow{3}{*}{-} \\  & detection latency). & \\  & Lightweight feature extraction. & \\ \hline \multirow{3}{*}{[47]} & Robust to imbalanced data. & \multirow{3}{*}{The evaluation is conducted used the NSL-KDD dataset, which is not dedicated to IoVs and is primarily an intrusion detection dataset.} \\  & Effective in detecting unknown attacks. & \\  & & \\ \hline \multirow{3}{*}{[61]} & High accuracy in threat detection. & Complexity in tradeoffs between utility \\  & & and’privacy. \\ \hline \multirow{3}{*}{[62]} & Zero-day attack detection. & \multirow{3}{*}{Complexity in implementation and management} \\  & High detection rates. & \\ \cline{1-1}  & Multi-access Edge Computing (MEC) assistance. & \\ \hline \multirow{3}{*}{[63]} & Adversarial attack detection. & \multirow{3}{*}{Computational overhead.} \\  & Blockchain integration. & \\ \cline{1-1}  & High detection accuracy & \\ \cline{1-1}  & Lightweight feature extraction. & \\ \hline \multirow{3}{*}{[64]} & Robust to imbalanced Data. & \multirow{3}{*}{The evaluation is conducted used the CIC-IDS dataset, which is not dedicated to IoVs and is primarily an intrusion detection dataset.} \\ \cline{1-1}  & Handling class imbalance. & \multirow{3}{*}{2017, dataset which is not dedicated to IoVs and is primarily an intrusion detection dataset.} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative analysis of FL-based IDS for IoV: advantages and drawbacks.