## 4 Experimental Results and Analysis
### Results Analysis
#### 4.5.2 Fed-DTB Performance Under High-Complexity Conditions
For the 50-client setup, 31 clients were excluded, reflecting the model's strong capacity to stabilise performance under moderate complexity. In the 25-client configuration, 16 clients were excluded by round 10, further confirming the system's ability to improve dataset quality. In all configurations, the mean trust scores of the remaining clients increased steadily, demonstrating the effectiveness of the trust-based selection strategy. Figure 17: Performance metrics across different number of clients: (**a**) precision, (**b**) recall, (**c**) F1 Score, and (**d**) ROC-AUC. The computational efficiency of the system, as illustrated in Figure 19, demonstrates significant improvements across all configurations. In the 25-client configuration, the training time per round stabilised at 36.74 s after initial reductions, reflecting optimised training processes enabled by trust-based exclusions. The breakdown of computational components (Figure 20) shows that 96.6% of the time was allocated to computation, with minimal contributions from communication (0.1%) and overhead (3.3%), confirming the efficiency of the system's use of resources. For the 50-client setup, the training time stabilised around 57.03 s, following an initial peak of over 200 s. The breakdown (Figure 21) indicates that computation remained the dominant factor at 95.9%, with communication and overhead accounting for only 0.1% and 3.35%, respectively. This underscores the system's ability to handle increased client pools without sacrificing efficiency. In the 100-client configuration, the training time stabilised at approximately 95.6 s after peaking above 391 s in the early rounds. Figure 22 highlights that computation dominated at 98.4%, with overhead contributing 1.4% and communication only 0.1%. This demonstrates the system's scalability and effective management of computational resources, even in high-complexity scenarios. Figure 19: The training time per round for configurations of 25, 50, and 100 clients. Figure 18: Trust mechanism effectiveness for configurations of 25, 50, and 100 clients. Finally, Table 2 comprehensively summarises the system's performance metrics across all configurations (25, 50, and 100 clients) for each round. The metrics include accuracy, loss, F1 Score, precision, the number of excluded clients, and total round time. This consolidated table demonstrates the system's ability to adapt to varying client loads, maintaining robust performance, strong classification metrics, and efficient processing times across different levels of complexity. The results validate the proposed framework's scalability, resilience, and adaptability, effectively handling moderate to highly complex federated learning scenarios. Table 2 also delineates the operating envelope we explored. Within each fleet size, the accuracy rises sharply in the first two rounds, once malicious clients are excluded, and then stabilises. When comparing final figures across configurations, the accuracy stays essentially flat (0.881 for ten clients, 0.872 for fifty, and 0.862 for one hundred), while the mean round-time extends from 13.97 s to 160.72 s. This exposes a clear throughput-latency trade-off; beyond about fifty vehicles, additional participants add less than one percentage point of reliability but more than double the turnaround time. The same numbers serve as a stress test. Even under the heaviest load examined (100 clients), the global model settles at 0.862 accuracy; the price is roughly 2.7 min of latency, acceptable for fleet-level analytics but restrictive for sub-second safety loops. Figure 21: The average time distribution for configurations of 50 clients. Figure 20: The average time distribution for configurations of 25 clients.