## 4 Experiment and Results
### Dataset  
#### 4.2.1 Data Definition and Introduction  
The dataset used in this study includes a portion of the publicly available D-fire dataset and images collected through direct web crawling. The D-fire dataset is a public dataset of real-world images for fire and smoke detection, divided into fire-only images, smoke-only images, images with both fire and smoke, and normal images. The dataset is collected from real-world environments and is labeled with high quality, and duplicate images were removed to increase the reliability of the data. It reflects a wide variety of situations to help models work accurately in the real world, and the large dataset provides reliable performance in complex situations. The D-fire dataset is divided into four main categories. First, fire-only images are those where the fire phenomenon is clearly visible. Second, smoke-only images are those where there is no fire, but smoke is captured. Smoke is an important early sign of a fire and is used to increase the sensitivity of prevention and detection systems. Third, images with both fire and smoke reflect a complex situation and help model real-world fire scenes. Finally, normal images represent situations where there is no fire or smoke at all and are used to train the system to avoid raising false alarms. After collecting images from the D-fire dataset, the collected images were thoroughly reviewed to remove as many duplicates as possible, and unnecessary images were deleted to prevent duplicates from negatively impacting model training. A web crawl was performed on the fire, smoke, and normal images to obtain additional data for later experiments. The web crawl was performed by automatically collecting images from various sources based on specific keywords, and the crawled images were then categorized into fire, smoke, and normal conditions. The D-fire dataset and the data collected from the web crawl were merged to form the final dataset for the experiments by deleting images that were not real, for example, images of fire or Photoshopped images. Figure 8 shows a graph of the number of images with corresponding labels: 6144 normal images, 1216 smoke images, and 2549 fire images, totaling 9909 images. In general, when collecting image data in a distributed environment, fire and smoke are less frequent than normal situations; we took this fact into account when collecting data. Figure 9 shows sample images from the dataset according to their labels. \begin{table}
\begin{tabular}{c c} \hline
**Hardware Environment** & **Software Environment** \\ \hline CPU: Intel Core i7-10700F 2.90 GHz & Operating system: Windows 10 Pro \\ GPU: NVIDIA GeForce RTX 3070 & Python: 3.11.9 \\ RAM: 32 GB & Pytorch: 2.4.0+cu121 \\ CUDA: 12.1 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental environment hardware and software. Figure 8: Comparison of the number of data labels. Figure 9: \(Cont\). This experiment was designed based on prior research showing that federated learning on small datasets can work well with fewer than 10 clients [44]; while federated learning typically assumes an environment with many clients, prior research has shown that sufficient learning results can be achieved with fewer clients without significant performance degradation. For this reason, we limit the number of clients to less than 10 to evaluate the performance and efficiency of federated learning. In this experiment, we configured one server and 3 clients, 5 clients, and 10 clients to compare the impact of different numbers of clients on associative learning. Table 2 shows the distribution of image data used by each client and server by label. All clients and servers used the same 3000 images, but each client set a different percentage of data per label to reproduce a non-independent and identically distributed (Non-IID) environment. By non-IID, we mean that each client does not have the same distribution of data and contains data biased toward certain classes.