## 3 State of the Art
### Analysis and Discussion
The variations mentioned above highlight the varied approaches that researchers have utilized to enhance the effectiveness of IDSs within the rapidly changing environment of IoV. In summary, while synchronous FL was a prevalent technique in the suggested solutions, some studies acknowledged the advantages of asynchronous methods, particularly in environments characterized by frequent updates and potential bottlenecks. * Evaluation metrics: In most of the papers that were reviewed, the evaluation of the efficacy of FL-based IDS systems relied on ML measures that assess the effectiveness of the analytic model. These metrics include accuracy, precision, recall, and F-measure. A limited number of research publications examined the effects of FL. In particular, ref. [55] discussed the consensus time, which is impacted by the quantity of FL workers and the number of created blocks. The study additionally assessed the effectiveness of the FL-enabled edge node by manipulating the reward and accuracy of the local model. This evaluation considered various elements, including the reward, energy consumption, and processing overhead. Moreover, the researchers did not overlook the significance of accuracy as a fundamental measure for evaluating the efficacy of their proposed solution. The paper also addressed the issues associated with recruiting FL workers, highlighting the possibility of bias and imbalance when selection is primarily predicated on reputation. The authors proposed various strategies to address these difficulties, such as including randomization in the selection procedure. In addition, in [56], the authors considered the \"number of global aggregations (NGA)\" as an evaluation metric. They presented information regarding the number of global aggregations performed in the proposed system and other state-of-the-art baseline frameworks. Their research demonstrated how many global aggregations are necessary for different numbers of communication rounds (R) to achieve the desired level of accuracy. The FLEMDS framework proposed in the study necessitates a reduced number of global aggregations in comparison to the baseline frameworks to attain a comparable level of accuracy. * Aggregation model: In the domain of distributed ML, the combination of data or model updates from several nodes holds significant importance in determining the overall performance and efficiency of the system. The aggregation process has been extensively explored in contemporary research, with numerous novel approaches and models offered in recent research papers. These aggregation models aim to successfully harness the collective intelligence of all participating nodes while simultaneously overcoming problems such as data heterogeneity, communication overheads, and adversarial threats. The examined literature suggested a range of aggregation models to improve the effectiveness and precision of distributed systems, particularly in the domain of FL. One of the most common aggregation models used in the reviewed papers is the federated averaging method, where local model updates are averaged to produce a global model [16; 53; 55; 58; 60; 62]. This approach is simple yet impactful, particularly in situations involving non-identically and independently distributed (non-IID) data [32]. An alternative methodology uses weighted federated averaging, as described in several papers [47; 54; 56]. This technique involves assigning varying weights to local models, considering factors such as the quantity of data samples or the quality of the model. Secure aggregation is another widely employed model aggregation technique in the field of FL, as observed in [52]. In this technique, various cryptographic techniques, including SMPC, are employed to consolidate data while preserving the confidentiality of the unprocessed updates. The authors of [64] used the Bagging Classifier technique as aggregation model in their developed solution. This technique aggregates the predictions of multiple models to produce a single, more accurate model.