## 3 State of the Art
### Analysis and Discussion
* Data distribution: While imbalanced data distribution is a significant concern in ML and AI research, most of the research papers we reviewed did not address this aspect. We only identified five articles, namely [47; 52; 53; 60; 64], that specifically addressed the issues and implications associated with imbalances in data distribution in FL scenarios. They stressed the importance of dealing with this problem to achieve robust and stable model performance. The authors of [52] emphasized that in real FL contexts, the data distributed across many nodes or devices may exhibit non-IID characteristics. These characteristics sometimes arise due to an imbalanced distribution of data, wherein certain data classes may be overrepresented in one node while being underrepresented in another. To overcome this difficulty, the study suggested an IDS that uses FL to help handle imbalanced data distribution. The authors of [53] examined the vulnerability of models to adversarial attacks, particularly when confronted with data imbalance. The presence of an imbalance in vulnerability can be exploited by adversarial examples, resulting in the misclassification of benign data. The authors presented various techniques for identifying these adversarial examples, indirectly addressing the difficulties associated with data imbalance. From another perspective, the authors of [60] showed that data distribution among vehicles in FL scenarios, particularly in the context of IoV, might exhibit a significant imbalance. This imbalance can potentially impact the overall performance of the global model. The paper introduced various methodologies aimed at alleviating the repercussions of this imbalance, thereby ensuring the robustness of the FL framework. The issues presented by imbalanced data distribution were also addressed in [47]. The authors highlighted the potential emergence of unexpected attack behaviors in the context of IoV development. The absence of comprehensive analysis and systematic gathering of various attack behaviors has resulted in an imbalanced distribution of sample data categories within intrusion detection for IoV. Consequently, this disparity leads to diminished accuracy in detection. The authors proposed an intrusion detection approach integrating FL and a memory-augmented autoencoder (FL-MAAE) to tackle this issue. They have considered the problem posed by imbalanced data distribution in their produced solution, hence ensuring the continued effectiveness of the model. Lastly, the proposed framework in [64] employs the Synthetic Minority Over-sampling Technique (SMOTE) to tackle the issue of class imbalance in the dataset. This approach of oversampling minority classes helps to create a more balanced dataset, which in turn allows for a more accurate and representative evaluation of the classification models. Addressing data imbalance is critical for guaranteeing the resilience and dependability of ML models, particularly in distributed learning scenarios such as FL. * The overhead: One of the primary issues frequently encountered in the domain of IDSs based on FL is the significant overhead associated with these systems. The effectiveness and responsiveness of IDSs in IoV contexts can be significantly affected by overhead, including computing, communication, and storage expenses. Addressing this overhead is crucial to ensure the seamless operation of these systems without compromising their primary function of identifying and mitigating threats. Several of the reviewed papers examined the issue of overhead, which holds significant importance in the field of distributed systems and FL [52; 53; 55; 56; 57; 60]. In [52], the term \"overhead\" refers to the complexity of the algorithms offered, and the authors stressed how important it is to reduce this complexity as much as possible to ensure efficient operations. In addition, ref. [53] discussed overhead in the context of communication costs, emphasizing the relevance of minimizing overhead to improve system performance.