## 5 Results
### First Test Case  
Our system integrates digit change detection and model training, as well as FL. To evaluate its performance, we design the first test case with two experimental scenarios. The first scenario serves as a baseline for comparison with the second, focusing exclusively on model training using two datasets. The second scenario incorporates the benefits of FL. Both scenarios are illustrated in Figure 10.  
In Scenario 1, shown in Figure 10, we implement a two-stage training process for the model on individual devices. The first stage is started by gathering N images, which are  
Figure 10: The first test case comprises two distinct scenarios representing different training methodologies, one incorporating federated learning (FL) and the other without its use. (**a**) Scenario 1, which does not use FL. (**b**) Scenario 2, which utilizes averaging methodology for FL. sent to a global server for labeling. These labeled digit images are subsequently used for model training on the device in the first iteration. In the second stage, a new batch of N images is collected and labeled in the same manner, allowing for further model training. The described procedure is repeated across all C devices, leading to C distinct models. Scenario 2, depicted in Figure 10b, introduces FL following each training stage. Initially, each device trains its model using the digit image dataset it collected. Afterward, the devices share their model parameters through a federated averaging methodology as described in Section 4.4. After parameter sharing, each device continues training its model on the next batch of digit images, resulting in further refinement of all individual device models. The first averaging process is more substantial, as the models originate from diverse random seeds and datasets, leading to considerable variance among them. In contrast, the second averaging is more conservative, since the models have become more similar despite the additional training. The described scenarios were evaluated using ten client devices (C = 10). Each device was assigned a combination of two datasets, each containing 150 images, leading to two datasets of 900 digits, referred to as D1 and D2. These datasets are the same for both scenarios. Furthermore, an additional testing dataset was created consisting of 500 images (3000 digit images in total) and designated for testing both scenarios at each stage. Furthermore, all training sessions consist of 25 epochs. The results presented in Table 2 display the accuracy calculated on this testing dataset. The row labeled S = 0 and T = D1 in Table 2 report the accuracy of models trained solely on dataset D1. The next row, marked with S = 1 and T = D2, presents the results obtained after completing the first scenario. These results were derived from consecutive training on D1 and D2 without utilizing FL. The improved performance observed in the second iteration of FL using the averaging methodology can be attributed to the distinct initialization conditions of the models. Initially, the models are configured with randomized weights and biases, resulting in substantial heterogeneity across individual models. Following the first round of training, these models diverge considerably in their weight and bias configurations. Consequently, averaging these disparate models produces a global model that differs notably from any individual local model, which can lead to a less effective aggregation in the first iteration of FL. In contrast, the second training iteration begins with a unified global model, meaning that each local model in this round is derived from a common starting point. As a result, the individual models exhibit variations of a shared structure, and differences between them are significantly reduced compared to the first iteration. Thus, averaging in the second iteration causes less disruption to the individual models, enabling a more cohesive integration. This approach facilitates a more effective distribution of learned knowledge across devices.