## 4 Experiment and Results
### Results  
As a result of having three clients, Table 3 lists the NMI values for each client per round. Table 4 lists the silhouette scores for each client per round, and Table 5 lists the accuracy values for the server and each client per round. As a result of having five clients, Table 6 lists the NMI values for each client per round. Table 7 lists the silhouette scores for each client per round, and Table 8 lists the accuracy values for the server and each client per round. Figure 11: Two-dimensional (2D) PCA. Figure 10: Confusion matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Round** & **Client 1** & **Client 2** & **Client 3** & **Client 4** & **Client 5** \\ \hline
0 & 0.859 & 0.768 & 0.872 & 0.868 & 0.878 \\
1 & 0.827 & 0.740 & 0.844 & 0.854 & 0.865 \\
2 & 0.822 & 0.739 & 0.847 & 0.858 & 0.868 \\
3 & 0.811 & 0.718 & 0.839 & 0.858 & 0.858 \\
4 & 0.799 & 0.701 & 0.825 & 0.851 & 0.850 \\
5 & 0.790 & 0.686 & 0.812 & 0.846 & 0.841 \\
6 & 0.783 & 0.675 & 0.799 & 0.840 & 0.821 \\
7 & 0.766 & 0.655 & 0.777 & 0.821 & 0.801 \\
8 & 0.754 & 0.643 & 0.762 & 0.807 & 0.790 \\
9 & 0.738 & 0.625 & 0.749 & 0.801 & 0.776 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: NMI values of each client over epochs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Round** & **Client 1** & **Client 2** & **Client 3** \\ \hline
0 & 0.554 & 0.448 & 0.639 \\
1 & 0.708 & 0.630 & 0.757 \\
2 & 0.769 & 0.718 & 0.806 \\
3 & 0.809 & 0.763 & 0.836 \\
4 & 0.834 & 0.796 & 0.852 \\
5 & 0.850 & 0.818 & 0.864 \\
6 & 0.862 & 0.834 & 0.869 \\
7 & 0.877 & 0.856 & 0.888 \\
8 & 0.874 & 0.856 & 0.876 \\
9 & 0.878 & 0.859 & 0.879 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Silhouette scores of each client over rounds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Round** & **Client 1** & **Client 2** & **Client 3** \\ \hline
0 & 0.859 & 0.768 & 0.872 \\
1 & 0.827 & 0.740 & 0.844 \\
2 & 0.822 & 0.739 & 0.847 & 0.858 & 0.868 \\
3 & 0.811 & 0.718 & 0.839 & 0.858 & 0.858 \\
4 & 0.799 & 0.701 & 0. As a result of 10 clients, Table 9 lists the NMI values for each client per round. Table 10 lists the silhouette scores for each client per round, and Table 11 lists the accuracy values for the server and each client per round. First, based on NMI, we see that with three clients, the clustering quality is stable, with NMI scores mostly staying above 0.80. This means that the local models generated by each client reflect the overall data well. However, with five clients, the NMI fluctuates between 0.70 and 0.85, and the variation in clustering quality between clients increases. This suggests that as the data are distributed across more clients, the quality of clustering may decrease on some clients. With 10 clients, the NMI score varies widely from the low 0.60s to 0.80, suggesting that as the number of clients increases, the data distribution becomes more unbalanced and clustering performance is likely to deteriorate. In particular, some clients may have very low NMI scores. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Round** & **Client 1** & **Client 2** & **Client 3** & **Client 4** & **Client 5** \\ \hline
0 & 0.554 & 0.448 & 0.639 & 0.573 & 0.565 \\
1 & 0.710 & 0.650 & 0.755 & 0.736 & 0.728 \\
2 & 0.766 & 0.723 & 0.798 & 0.789 & 0.784 \\
3 & 0.802 & 0.769 & 0.827 & 0.829 & 0.819 \\
4 & 0.831 & 0.801 & 0.847 & 0.858 & 0.844 \\
5 & 0.850 & 0.826 & 0.857 & 0.877 & 0.857 \\
6 & 0.862 & 0.844 & 0.861 & 0.890 & 0.869 \\
7 & 0.865 & 0.849 & 0.860 & 0.897 & 0.872 \\
8 & 0.869 & 0.857 & 0.860 & 0.901 & 0.875 \\
9 & 0.870 & 0.863 & 0.860 & 0.902 & 0.875 \\ \hline \end{tabular}
\end{table} 
Table 7: Silhouette scores of each client over epochs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Round** & **Server** & **Client 1** & **Client 2** & **Client 3** & **Client 4** & **Client 5** \\ \hline
0 & 0.9997 & 0.965 & 0.936 & 0.976 & 0.976 & 0.968 \\
1 & 0.991 & 0.953 & 0.922 & 0.968 & 0.973 & 0.963 \\
2 & 0.9977 & 0.952 & 0.922 & 0.968 & 0.974 & 0.965 \\
3 &  
We also see differences in terms of silhouette score.