## 4 Experiment and Results
### Results
For NMI, we can see that it decreases for all clients but remains at 0.6. Silhouette score shows an increase over the rounds for all clients, reaching 0.9. For accuracy, we can see that the server has the highest accuracy, and it does not change much over the rounds. In this study, the confusion matrix was used for visualization. Figure 15 shows the confusion matrices of the server and clients. From the figure, we can see that the proposed model classifies most cases correctly. From the application of the bisecting K-means algorithm on the server, we see that the algorithm correctly classifies each label for all but 1 of the 3  
Figure 14: Accuracy line chart of the server and clients: (**a**) Server; (**b**) Client 1; (**c**) Client 2; (**d**) Client 3. Figure 15: Confusion matrices of the server and clients: (**a**) Server; (**b**) Client 1; (**c**) Client 2; (**d**) Client 3. In this study, high-dimensional data such as images were visualized in two dimensions using the PCA technique. Figure 16 shows the 2D PCA visualization of the data distribution of each class in a 2D space. In the figure, squares represent fire; triangles, smoke; and circles, normal; the predicted classes are visualized as circles with the same color as the actual symbols. The figure shows that the proposed model separates the classes well and provides a clear understanding of the data structure and clustering performance. We ran our experiments in the same setup as ViT, using ResNet18 as the CNN backbone. To compare the performance of a CNN and ViT, Table 12 presents the NMI values of each client per round, which allows us to analyze the difference in clustering quality. In addition, Table 13 compares the silhouette scores of each client per round to evaluate the degree of intracluster cohesion and intercluster separation. Finally, Table 14 shows the overall performance change through the server accuracy values per round. According to the CNN-based experimental results, the clustering accuracy of the server performed relatively well at 0.587 in the early rounds, but the performance gradually decreased as the rounds progressed. The NMI value of each client was generally low, with a continuous downward trend from 0.334 to 0.087. This resulted in poor clustering quality for the clients. Silhouette scores were also low, averaging around 0.3 for each client, indicating that the boundaries between clusters were not clear and data within clusters were not well separated. In contrast, the ViT-based experiments showed much more stable and superior performance compared to the CNN. The ViT model achieved high clustering accuracy from the early rounds and showed little performance degradation as the rounds progressed. In the final round, the ViT model maintained a high clustering accuracy of 0.9787, and the NMI values of the clients decreased slightly from 0.859 to 0.744 but still remained high. Silhouette scores were also above 0.8 for all clients, with well-defined boundaries between clusters and clear separation of data within clusters. ViT outperformed the CNN not only in clustering accuracy but also in NMI and silhouette scores, and it showed stable clustering performance even in the presence of data imbalance. The proposed model achieves 98% classification accuracy for fire, smoke, and normal classes in an FL structure in a non-IID environment. The images used in the experiments are real images, improving the model's applicability for industrial sites. This model can make a significant contribution in the event of a fire. The proposed model provides reliable data and highly accurate fire classification and is expected to play an important role in building a system for the early diagnosis of fire in industrial sites.