## 5 Simulation Results and Discussion
### Results Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Client (CL)** & **Dataset** & **Data Size** & **Local Accuracy (\%)** & \
\begin{tabular}{c} **Weight Update** \\ **Magnitude** \\ \end{tabular} &
\begin{tabular}{c} **Communication** \\ **Overhead (\%)** \\ \end{tabular} \\ \hline CL1 & CIC-DDoS2019 & 112,000 & 96.7 & 0.038 & 11.5 \\ \hline CL2 & CIC-DDoS2019 & 110,500 & 97.0 & 0.036 & 12.1 \\ \hline CL3 & CIC-DDoS2019 & 110,833 & 96.9 & 0.037 & 10.8 \\ \hline CL4 & UNSW-NB15 & 111,200 & 96.5 & 0.035 & 10.9 \\ \hline CL5 & UNSW-NB15 & 112,700 & 96.8 & 0.037 & 11.2 \\ \hline CL6 & UNSW-NB15 & 109,433 & 96.4 & 0.034 & 9.7 \\ \hline CL7 & IoT23 & 110,100 & 97.2 & 0.039 & 13.0 \\ \hline CL8 & IoT23 & 111,500 & 96.8 & 0.036 & 11.6 \\ \hline CL9 & IoT23 & 111,167 & 96.5 & 0.037 & 10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Client-specific contribution analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Round No.** & **Loss** & **Global Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
1 & 0.310 & 91.2 & 90.5 & 90.9 & 90.7 \\ \hline
5 & 0.260 & 93.6 & 92.8 & 93.2 & 93.0 \\ \hline
10 & 0.210 & 95.5 & 94.6 & 95.0 & 94.8 \\ \hline
20 & 0.170 & 97.0 & 96.2 & 96.5 & 96.4 \\ \hline
30 & 0.140 & 98.1 & 97.8 & 97.9 & 97.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Global model convergence across communication rounds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Client (CL)** & **Dataset** & **Computation Time (s)** & **Memory Usage (MB)** & **Energy Consumption (\&Wh)** & **Optimization Efficiency Score (OES)** \\ \hline CL1 & CIC-DDoS2019 & 130 & 210 & 0.38 & 0.91 \\ \hline CL2 & CIC-DDoS2019 & 140 & 220 & 0.40 & 0.90 \\ \hline CL3 & CIC-DDoS2019 & 125 & 200 & 0.36 & 0.92 \\ \hline CL4 & UNSW-NB15 & 150 & 230 & 0.42 & 0.89 \\ \hline CL5 & UNSW-NB15 & 135 & 210 & 0.39 & 0.91 \\ \hline CL6 & UNSW-NB15 & 120 & 200 & 0.35 & 0.93 \\ \hline CL7 & IoT23 & 140 & 220 & 0.40 & 0.90 \\ \hline CL8 & IoT23 & 130 & 210 & 0.38 & 0.91 \\ \hline CL9 & IoT23 & 125 & 200 & 0.36 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimization efficiency per client. distribution is diverse due to somewhat different data sizes. Local accuracy is over 96% for all customers, with fairness indices regularly above 0.89. The global model impact indicator shows each client's balanced contribution to federated learning. This fairness guarantees that no customer disproportionately influences the model, fostering cooperation. Table 10 compares the execution time of several FL-based intrusion detection methods. Training time, inference time, communication overhead, and execution time are assessed. FL ResVGG-SwinNet has the quickest overall execution time of 211.6 s because of its computationally efficient design and federated-learning procedure. Because ResVGG-SwinNet balances feature extraction and contextual attention with low redundancy, training time is decreased to 195.3 s. In addition, its 2.8 ms inference time and 13.5 s transmission overhead demonstrate its outstanding performance in managing dispersed datasets among clients. Hybrid LSTM-GRU models and Federated Sequence Learning (FSL) take longer to execute owing to training and inference overheads. For instance, FSL takes 281.9 s to execute, indicating lesser performance. Table 11 compares federated-learning strategies across many assessment metrics using an extensive statistical analysis. [PERSON], [PERSON]'s correlation, ANOVA, [PERSON]'s rank correlation, Kruskal-Wallis, Student's \(t\)-test, Mann-Whitney U test, [PERSON] post hoc test, [PERSON]'s tau, and Chi-Squared tests provide a holistic view of model performance reliability and robustness in the table. These measures assess model predictions' consistency, correlation, and variance importance. FL ResVGG-SwinNet surpasses other methods with the lowest Wilcoxon and Mann-Whitney scores of 2.121 and 1.321, showing more consistency and less variation among predictions.