## 5 Results
### Second Test Case  
The second test case is designed to simulate real-world scenarios in which devices are deployed and trained in batches at different time intervals, as illustrated in Figure 11. In this test case, ten clients are divided into two batches of five clients each. The first batch consists of the initially deployed devices, following a training process similar to Scenario 2. These devices collect an initial dataset of N images and train their initial, randomized models on these data. The next step involves FL using a model averaging approach, followed by another cycle of data collection, model training, and FL. As a result, the cloud stores a second version of the global model, generated after two rounds of training. Subsequently, the second batch of devices is introduced. Their initial model is set to the global model available at that point. These devices follow the same process of two rounds of dataset collection (each with N images), model training, and FL updates using the averaging methodology. The key difference is that for these FL updates, the cloud incorporates all the latest models produced during training. This includes models from the first batch, which were generated after training on their second dataset. Figure 11: Second test case displaying training scheme where second batch of devices is trained based on results of training on first batch. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average** & **S = 0, T = D1** & **S = 1, T = D2** & **S = 2, T = Avr1** & **S = 2, T = D2** & **S = 2, T = Avr2** \\ \hline
**Accuracy** & 87.40 & 91.41 * & 75.67 & **93.60** & 93.54 \\ \hline \hline \end{tabular}  
*—Average does not include accuracy for device client 5. The bold values represent maximal values in row.  
\end{table} 
Table 3: Average accuracy for both scenarios. The evaluation of the second test case is conducted using the same parameter settings as in the first test case. The number of images per training dataset, N, is set to 150, while the test set consists of 500 images. Each image is divided into six digit images, resulting in 900 digit images per training dataset and 3000 digit images for the testing dataset. Furthermore, each training session consists of 25 epochs. The accuracy evaluation results on testing dataset are presented in Table 4.  
The columns labeled T in Table 4 indicate the point at which model accuracy was evaluated. D1 refers to accuracy following training on the first dataset, while D2 represents results after the second training session on the second dataset. Rows labeled as D2* contain repeated results for devices in Batch 1 after training on the second dataset. Finally, rows beginning with F represent the results following FL updates. As observed in the first test case, the initial FL update leads to a reduction in model accuracy. As in the first test case, a similar trend is evident in the second FL update, where it is shown that the second FL iteration did not significantly alter the overall average accuracy. However, the inclusion of models from the second batch highlights the importance of FL updates. As shown in the columns labeled Batch 2 in Table 4, the training results after the first dataset are notably higher than those from Batch 1. This suggests that FL plays a critical role in generating global models that serve as robust initial configurations for newly deployed devices. Moreover, performing FL updates using the latest models from all devices results in improved global models. This is evident in the final row, which shows the accuracy of the final global model deployed to each device, with most columns exhibiting their highest accuracy in this row. This finding is significant for two reasons. First, even though training was stopped for devices in Batch 1, the FL update considerably improved their accuracy. Second, devices in Batch 2 also experienced an increase in accuracy, reaching their maximum values.