## 4 Experimental Results and Analysis
### Dataset Distribution  
Our experiments rely on three datasets to capture the heterogeneity, dimensionality, and adversarial conditions characteristic of real-world IoV systems: MNIST Dataset: A well-known benchmark in machine learning [47] comprising 60,000 training images. We partitioned MNIST into clients using a non-IID scheme to reflect the uneven data distributions often encountered in IoV networks [45]. To approximate a fleet-scale workload while respecting practical memory limits, the MNIST training set is first expanded by systematic replication in proportion to the number of simulated clients; with fifty clients, this yields roughly \(4.8\times 10^{5}\) images. In raw-pixel terms (\(\approx\)\(3.8\times 10^{8}\) grayscale pixels), this is of the same computational order as the CIFAR-100 training set (50,000 colour images, \(\approx\)\(1.5\times 10^{8}\) RGB pixels). The resulting memory footprint proved tractable on the desktop simulation workstation used in this study (single 8 GB GPU) and would also fall within the 2 GB VRAM envelope typical of an in-vehicle ECU. The enlarged MNIST set is then partitioned with a Dirichlet allocation (concentration 0.4), producing intentionally unbalanced label mixes that mirror the skew observed in operational IoV fleets. This procedure supplies both volume and heterogeneity without additional data collection overhead. CIFAR-10  
Figure 4: Experimental setup and data flow. Dataset: This dataset introduces additional complexity and higher-dimensional images, simulating scenarios where vehicles collect more diverse visual data [48]. Synthetic IoV Dataset: Inspired by empirical observations and prior vehicular studies [49; 50; 51; 52], we generated this dataset to mimic factors unique to connected vehicles, including signal strength, connection stability, vehicle health, and user feedback metrics. The generation process follows statistical models consistent with wireless communication theory, where the signal strength degrades with distance; mechanical engineering principles, as the engine health deteriorates over time; and trust scores that evolve based on operational conditions and user experience rooted in human-computer interaction and adaptive systems theory. We performed a correlation analysis between key parameters to validate the realism and relevance of the dataset. The results, shown in Figure 5, confirm that the relationships among metrics such as connection stability, engine health, and trust score align with the expected behaviour of real-world IoV systems. Specifically, connection stability correlates with geographic proximity and traffic density, engine health impacts computational reliability for FL participation, and trust scores reflect historical performance consistency. These patterns align with studies of the established vehicular network [49; 50; 51; 52]. For instance, higher connection stability strongly correlates with trust scores (Figure 6), reflecting the system's robustness under reliable communication conditions. Similarly, satisfaction feedback is closely tied to trust scores, as Figure 7 shows the importance of user-centric parameters in evaluating vehicular reliability. Figure 5: Correlation matrix illustrating relationships among vehicle parameters. By injecting controlled anomalies (e.g., increased bandwidth usage, sporadic IDS alerts), the dataset provides a systematic and controlled foundation to mirror realistic adversarial scenarios frequently reported in IoV research. For example, urban-route simulations produce more interference, while rural conditions produce stronger average signal strength, as the correlation matrix supports. This approach enables a detailed and structured exploration of relationships and behaviours that might be difficult to isolate in real-world conditions. While these validations confirm the dataset's reliability and relevance for benchmarking federated learning models, it is important to acknowledge the inherent limitations of synthetic datasets.