## 3 State of the Art
### Analysis and Discussion
The resulting supermodel, created by the central server, exhibits better robustness than the individual edge device models. Each aggregation method provides specific benefits designed to address the challenges and requirements of dispersed learning settings. The models described above are at the forefront of current research, each tackling distinct issues. As technology advances and increasingly intricate situations arise, these models are expected to continue to develop, facilitating the implementation of more resilient and effective distributed learning systems. The ongoing investigation and advancement of aggregation modelsserve as evidence of the dynamic characteristics of ML research and its dedication to optimizing the utilization of distributed nodes' collective intelligence. * Optimization algorithms: The utilization of FL in IDSs presents a new and innovative method for addressing the issues related to data privacy and effective model training in IoV. Advanced algorithms play a pivotal role in optimizing FL models. For instance, the federated proximal algorithm has been used to fine-tune model parameters, ensuring optimal performance in detecting intrusions [52]. Similarly, some studies have adopted federated stochastic gradient descent (federated SGD) to optimize the parameters of the proposed IDS models [53; 55; 58]. Furthermore, some papers utilized other optimization techniques, such as the Adam optimizer [16; 47], a fuzzy logic-based technique [56], Bayesian optimization (BO) [60], and the federated averaging (FedAvg) algorithm [62]. The authors of [64] used the grid search method for hyperparameter tuning as an optimization algorithm in their solution. This method is employed to optimize the Cat Boost model, a gradient boosting algorithm that utilizes decision trees as the classifier model for edge devices. The grid search technique exhaustively searches over a specified set of hyperparameters to improve the model's accuracy. The integration of optimization approaches, combined with the decentralized nature of FL, holds the potential to deliver resilient and effective IDSs for the IoV environment. Decentralizing the learning process and applying complex optimization algorithms not only enhances detection capabilities but also ensures that modern concerns regarding privacy and efficiency within the IoV landscape are effectively addressed. This represents a significant advancement for the industry. The ongoing expansion and development of IoV necessitate the use of innovative strategies to ensure the protection and security of our networked automotive environment. * Real-time processing: A critical aspect of FL-based IDSs is their ability to process data in real time, ensuring timely detection and response to potential threats. Our review found several papers that proposed IDSs designed for real-time operation [52; 58; 59; 60]. For instance, refs. [52; 53; 58] highlighted the significance of real-time processing for IDSs, especially when dealing with vehicular networks. In addition, ref. [60] introduced ImageFed IDS, a system designed for real-time inference. It employs a lightweight image-based feature extraction for CAN packets, making it suitable for real-time applications. On the other hand, some papers supported a batch processing approach rather than real-time processing [16; 47; 56]. Some papers did not explicitly mention whether their proposed solutions are designed for real-time or batch processing. Nevertheless, all the papers emphasized the importance of real-time processing in IDSs for IoV, with various solutions and methodologies proposed to achieve this objective. The operational significance of IDSs for vehicle networks increases as these networks undergo continuous evolution and encounter a diverse range of cyber threats. The research presented in these papers offers solutions and approaches that contribute to the development of a more secure and responsive IoV environment by emphasizing the significance of real-time processing.