## 4 Experimental Results and Analysis
### Results Analysis
#### 4.5.2 Fed-DTB Performance Under High-Complexity Conditions  
The results from configurations with 25, 50, and 100 clients clearly analyse the system's performance under high-complexity conditions. The 25-client setup tested scal \begin{table}
\begin{tabular}{l c} \hline
**Metric** & **Value** \\ \hline Final Accuracy & 0.8809 \\ Final Loss & 0.7225 \\ Final Precision & 0.8965 \\ Final Recall & 0.8809 \\ Final F1 Score & 0.8824 \\ Final ROC-AUC & 0.9886 \\ Total Excluded Clients & 6 \\ Average Training Time & 3.2 s \\ \hline \end{tabular}
\end{table} 
Table 1: Summary statistics for 10 clients. Figure 14: Training time evaluation. Figure 13: Trust mechanism effectiveness. ability with moderate complexity, while the 50-client configuration introduced higher demands and risks from malicious clients. The 100-client evaluation further assessed performance in a large-scale, heterogeneous environment, demonstrating the system's robustness and effectiveness. Figures 15 and 16 show the accuracy and loss over 10 rounds for 25, 50, and 100 clients' configurations. Figure 15 (Accuracy Evaluation): The x-axis represents the rounds (1-10), while the y-axis shows accuracy values from 0.6 to 0.9 (60-90%). The y-axis range has been optimised for readability, focusing on the relevant performance range rather than starting from zero. Figure 16 (Loss Evaluation): The x-axis represents the rounds (1-10), while the y-axis shows loss values from 0.6 to 2.2. This range captures the complete loss trajectory from initial high values to final convergence. The client groups are distinguished by specific markers: 25 clients with an orange line (square markers), 50 clients with a green line (triangular markers), and 100 clients with a light red line (light blue \"X\" markers). The results indicate a clear progression in performance. Initially, the accuracy was low, ranging from 18.3% to 25.41% (though the figure's y-axis is scaled from 60 to 90% for enhanced readability), with high loss values between 2.1 and 2.2, reflecting the presence of malicious clients and noise in the dataset. As the system's trust-based exclusion mechanism operated, accuracy sharply improved, reaching values between 84.7% and 87.9% by the final round. Concurrently, loss values decreased significantly, stabilising between 0.6895 and 0.8092. These trends highlight the system's ability to adapt, exclude untrustworthy clients, and achieve consistent performance improvements across all client configurations. Figure 16: Loss evaluation for configurations of 25, 50, and 100 clients. Figure 15: Accuracy evaluation for configurations of 25, 50, and 100 clients. For the 25-client setup, the precision increased steadily, stabilising at 90.0%, while recall reached 85.2%, and the F1 Score achieved 86.09%. The ROC-AUC score remained consistently high, gaining 0.9872, reflecting the reliability of the system under moderate complexity. These metrics demonstrate the model's ability to classify malicious and non-malicious clients while maintaining robust performance accurately. In the 50-client configuration, the system handled increased complexity effectively, with precision stabilising at 91.2%, recall reaching 87.9%, and the F1 Score improving to 89.5%. The ROC-AUC score peaked at 0.9913, further highlighting the improved ability of the system to distinguish between benign and malicious clients under higher demands. For the 100-client configuration, which posed the highest complexity due to its larger and more diverse client pool, precision stabilised at 89.8%, recall reached 84.7%, and the F1 score achieved 87.1%. The ROC-AUC score reached 0.9859, highlighting the robustness and adaptability of the system even under challenging conditions. The evolution of the trust score for 10 rounds, as shown in Figure 18, highlights the system's ability to exclude malicious clients while maintaining robust performance steadily. In the 100-client configuration, the system excluded 56 clients by round 10, showcasing its effectiveness in managing large-scale conditions.