## 5 Conclusions  
A fire classification system using image clustering based on an FL structure is proposed herein. The proposed architecture focuses on optimizing the classification performance to reflect data characteristics through cooperation between the server and clients. The client data are non-IID to ensure that the performance is maintained in real-world environments. In the proposed system, a ViT model pretrained with server data is used to obtain initial weights that are sent from the server to the client. Using the ViT model, the server extracts data features and clusters the data using the bisecting K-means algorithm. The model then calculates the global center point and global distribution and generates the GCI. The client clusters the local data using the K-means algorithm based on the GCI sent to it and calculates new center points and data distributions. Then, it measures the difference in data distributions between the clients and server using KL divergence and calculates the style vector and alpha value, which are then delivered to the server. The values sent to the server are ensembled to improve clustering performance. The main contributions of the proposed architecture in this work are as follows:  
* An improved ViT-based fire classification model was proposed. An initial ViT model was built to achieve association learning to create a base model that can effectively classify fire, smoke, and normal situations. Using this model, both the server and clients achieved high accuracy; in particular, the server achieved 98% accuracy. * The proposed architecture maintained good performance even when each client's local data were non-IID distributed. As the rounds progressed, the NMI values of clients tended to decrease slightly but remained above 0.6, indicating that the clustering is consistently accurate. The silhouette score of each client shows an increasing trend as the rounds progressed, finally reaching 0.9, indicating increasing clustering quality. These results imply that the clients effectively classify and cluster local data through the GCI, which is the weight sent by the server. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Round** & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**ViT**} \\ \cline{2-6}  & **Client 1** & **Client 2** & **Client 3** & **Client 1** & **Client 2** & **Client 3** \\ \hline
0 & 0.475 & 0.450 & 0.542 & 0.554 & 0.448 & 0.639 \\
1 & 0.426 & 0.413 & 0.420 & 0.708 & 0.630 & 0.757 \\
2 & 0.414 & 0.398 & 0.405 & 0.769 & 0.718 & 0.806 \\
3 & 0.384 & 0.373 & 0.371 & 0.809 & 0.763 & 0.836 \\
4 & 0.346 & 0.351 & 0.336 & 0.834 & 0.796 & 0.852 \\
5 & 0.360 & 0.370 & 0.358 & 0.850 & 0.818 & 0.864 \\
6 & 0.369 & 0.382 & 0.368 & 0.862 & 0.834 & 0.869 \\
7 & 0.377 & 0.389 & 0.377 & 0.877 & 0.856 & 0.888 \\
8 & 0.381 & 0.393 & 0.382 & 0.874 & 0.856 & 0.876 \\
9 & 0.381 & 0.394 & 0.384 & 0.878 & 0.859 & 0.879 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Silhouette score comparison between CNN and ViT. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Round** & **CNN** & **ViT** \\ \hline
0 & 0.5873 & 0.9987 \\
1 & 0.5640 & 0.9843 \\
2 & 0.5477 & 0.9937 \\
3 & 0.5400 & 0.9940 \\
4 & 0.4987 & 0.9903 \\
5 & 0.4830 & 0.9890 \\
6 & 0.4790 & 0.9837 \\
7 & 0.4723 & 0.9967 \\
8 & 0.4650 & 0.9650 \\
9 & 0.4593 & 0.9787 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Server accuracy comparison between CNN and ViT. * The proposed model can be integrated into real-world fire classification systems for real-time detection. Experimental results show that the model can quickly and accurately classify fire, smoke, and normal conditions, making it an important contribution to fire prevention and response systems in industrial sites. It has high reliability because it utilizes real image data. By calculating KL divergence to indicate how similar each client's data are to the GCI, we proposed a way to measure and understand differences in data distributions, enabling clients to learn more precisely. In future research, we plan to pursue the following directions.