## 5 Simulation Results and Discussion
### Results Discussion
[PERSON]'s correlation of 0.075 and [PERSON]'s tau of 0.091 demonstrate high alignment between observed and predicted values, demonstrating model correctness and precision. The Newman-Keuls score of 1.104 and ANOVA score of 2.109 confirm its capacity to differentiate numerous groups with minimum error. The \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Technique** & **Training Time (s)** & **Inference Time (ms)** & **Communication Overhead (s)** & **Total Execution Time (s)** \\ \hline FL-LSTM [12] & 220.5 & 3.4 & 15.8 & 239.7 \\ Hybrid LSTM-GRU models [17] & 245.8 & 4.1 & 16.5 & 266.4 \\ Fedex [23] & 235.3 & 3.6 & 15.2 & 254.1 \\ Federated Sequence Learning (FSL) [24] & 260.7 & 4.3 & 16.9 & 281.9 \\ FedAGRU with attention mechanisms [25] & 210.4 & 3.1 & 14.7 & 228.2 \\ FeDPI platform with FL and autoencoders [21] & 230.2 & 3.5 & 15.5 & 249.2 \\ Collaborative FL framework [22] & 225.6 & 3.2 & 15.3 & 244.1 \\
**Proposed FL ResVGG-SwinNet** & **195.3** & **2.8** & **13.5** & **211.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Execution time comparison of different techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Client (CL)** & **Dataset** & **Data Size** & \
\begin{tabular}{c} **Local Model** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Global Model** \\ **Impact (\%)** \\ \end{tabular} & **Fairness Index** \\ \hline CL1 & CIC-DDoS2019 & 112,000 & 96.7 & 12.4 & 0.93 \\ \hline CL2 & CIC-DDoS2019 & 110,500 & 97.0 & 12.8 & 0.92 \\ \hline CL3 & CIC-DDoS2019 & 110,833 & 96.9 & 12.6 & 0.94 \\ \hline CL4 & UNSW-NB15 & 111,200 & 96.5 & 11.9 & 0.91 \\ \hline CL5 & UNSW-NB15 & 112,700 & 96.8 & 12.2 & 0.93 \\ \hline CL6 & UNSW-NB15 & 109,433 & 96.4 & 11.8 & 0.89 \\ \hline CL7 & IoT23 & 110,100 & 97.2 & 13.0 & 0.94 \\ \hline CL8 & IoT23 & 111,500 & 96.8 & 12.5 & 0.92 \\ \hline CL9 & IoT23 & 111,167 & 96.5 & 12.3 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Fairness in federated learning. suggested model's hybrid design captures geographical, contextual, and hierarchical data relationships, resulting in better performance. Federated learning enables data privacy and decentralised model optimisation. Dynamic feature selection and adaptive learning improve the proposed method's generalisation across IoT contexts. These advances make the model statistically reliable, making it a good candidate for IoT-based intrusion detection. Figure 15 illustrates the sensitivity analysis of the FL ResVGG-SwinNet model, examining how the learning rate multiplier (Parameter 1) and dropout rate multiplier (Parameter 2) affect accuracy. The 3D surface graphic shows how various settings affect model performance. Blue represents low sensitivity, while yellow-green represents excellent sensitivity. In parameter space, the surface shows stability and instability. The smooth gradient along the learning rate multiplier axis suggests small changes increase performance. The dropout rate multiplier is more variable, indicating its importance in limiting overfitting and guaranteeing generalisation. The interaction between optimum learning rate and dropout levels leads to peak accuracy around 98.5%. This chart shows how parameter adjustment affects performance and helps choose appropriate combinations. It shows FL ResVGG-SwinNet's ability to retain high sensitivity and accuracy throughout parameter adjustments.