## 4 Experiments and Results
### Evaluation
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Model** & **Scenario** & **Running Time** & **Memory Usage** & **Performance Summary** \\ \hline Decision Tree & All Features & 1.05 s & 1157.10 MB & Fastest model with all features but moderate memory consumption \\ \hline \multirow{4}{*}{} & ML Model (25 Features) & 0.82 s & 570.51 MiB & Faster with reduced memory (about 50\% memory saved) \\ \cline{2-5}  & FL Models (25 Features) & 2.75 s & 837.93 MB & Slightly slower but manageable memory usage in federated learning \\ \hline \multirow{4}{*}{Random Forest} & Avg. Round Per Client (FL) & 0.0075 s & 0.0456 MB & Efficient in FL with minimal memory and time per round consistent \\ \cline{2-5}  & All Features & 18.91 s & 1171.41 MB & Balanced performance, slower than Decision Tree but consistent \\ \hline \multirow{4}{*}{} & 25 Features & 14.02 s & 594.97 MiB & Noticeable speedup, memory reduction by 50\% \\ \cline{2-5}  & FL Models (25 Features) & 16.53 s & 954.91 MB & Similar performance to 25 features but slightly higher memory in FL \\ \hline \multirow{4}{*}{XGBoost} & Avg. Round Per Client (FL) & 0.8199 s & 0.1748 MB & Moderate time and memory usage per round in FL \\ \cline{2-5}  & All Features & 60.24 s & 1186.96 MB & Much slower execution with high memory consumption \\ \cline{2-5}  & 25 Features & 13.82 s & 612.95 MiB & Dramatic improvement in execution and memory usage \\ \hline \multirow{4}{*}{} & FL Models (25 Features) & 14.10 s & 851.43 MB & Stable performance in FL, faster than full-feature version \\ \cline{2-5}  & Avg. Round Per Client (FL) & 0.4015 s & 0.3639 MB & Moderate time with slightly higher memory in FL \\ \hline Gradient Boosting & All Features & 460.57 s & 1194.47 MB & Extremely slow compared to all other models with high memory demand \\ \hline \multirow{4}{*}{} & 25 Features & 358.78 s & 617.83 MiB & Better performance with feature reduction, but still much slower than others \\ \cline{2-5}  & FL Models (25 Features) & 400.20 s & 862.06 MB & Similar slow performance in FL, but decent memory savings compared to all features \\ \hline \multirow{4}{*}{KNN} & Avg. Round Per Client (FL) & 1.5169 s & 0.1964 MB & Longer time with moderate memory in FL \\ \cline{2-5}  & All Features & 11.78 s & 1291.00 MB & Quick execution but highest memory usage with all features \\ \hline \multirow{4}{*}{} & 25 Features & 0.23 s & 627.52 MiB & Fastest model with lowest memory usage drop (50\%) \\ \cline{2-5}  & FL Models (25 Features) & 0.21 s & 838.70 MB & Continues to be the fastest even in federated learning, though memory usage increases \\ \hline \multirow{4}{*}{} & Avg. Round Per Client (FL) & 0.0022 s & 0.0452 MB & Fastest in FL with minimal memory usage \\ \cline{2-5}  & The color coding in the table provides a quick visual guide to performance levels. Green shows the best or most efficient performance, yellow indicates moderate or satisfactory performance, and red highlights areas where performance is the weakest or most resource-intensive. & \\ \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of machine learning models under different scenarios. These analyses emphasize the importance of feature reduction in optimizing both memory usage and running time while also showcasing the efficiency of federated learning with modest computational overhead.