## 4 Experimental Result  
The research experiment was conducted on a portable computer with the configuration features of a Core i5 processor, 4 GB of main memory, and 2.4 GHz of CPU momentum that was running Microsoft Windows 11. The programming environment utilized Jupyter Notebook and Kaggle Notebook, both with Python 3. Furthermore, the Colab at Google acted as a program framework to conduct the experiment. In the following section, the outputs are discussed and compared with other frameworks. The envisioned infrastructure built on FL was allocated among four end-users along with the global framework. The findings were assembled from the local ends utilizing the aggregator strategy of FedAvg. The resultant products produced during the testing cycles are illustrated in Figure 5.  
Figure 5 depicts the accuracy trends of a federated learning model across many clients over a number of repetitions of the local training and sharing of the feedback with a global server. The horizontal axis depicts the number of repetitions, indicating the advancement of training, and the vertical axis represents accuracy in percentage terms, representing the model's performance. Every curve in the figure displays an individual client; the blue line denotes client-1, the orange line denotes client-2, the green line denotes client-3, and the red line represents another client, showcasing how their respective models improve over time. The accuracy deviations could be attributed to differences in data distributions, local model updates, or computing resources. The graph in Figure 5 illustrates the converging behavior of federated learning, demonstrating whether all clients attain equal performance levels or if there are differences because of disparities in data or system-related constraints. From Figure 5, we can observe that client-1 achieved 87.5% accuracy in its first time execution of the model; then, it grew gradually, by 88%, 88.06%, 88.18%, and 88.31%, in the second, third, fourth and fifth time executions, respectively. On the other hand, client-2 acquired 87.81%, 88.06%, 88.31%, 88.43%, and 88.5% in the first time execution to the fifth time execution, respectively. On the other hand, client-3 showed an efficacy of 88%, 88.31%, 88.5%, 88.56%, and 88.68% in each execution and also performed the best among all the local clients. Finally, client-4 achieved a success rate of 87.56% in the first execution of the model on its end, and then it achieved 88.18%, 88.37%, 88.5%, and 88.63% in its second, third, fourth, and fifth time runs on the model, respectively. In conclusion, we can state from Figure 5, that each local client performed better with each time execution of the model, and client-3 showed the highest efficacy growth over time. This approach is important in federated learning because it helps evaluate model consistency across clients, discover potential fairness issues, and ensure that no client suffers a great disadvantage during training. The detected trends can also be used to inform optimization tactics, such as modifying learning rates, enhancing aggregation approaches, and dealing with straggler effects. Figure 5: Accuracy trends of federated learning clients over five iterations. The feature importance visualisations that were created using the SHAP framework are summarised in Figure 6. From Figure 7a, we can observe that the suggested method has an 88.4% achievement rate during the training session and an 88.2% achievement rate during the testing session. Furthermore, Figure 7b represents the loss calculation of the model after aggregating all models with a loss value of 26.6% in the training phase and a 27.7% loss value in the testing phase. In the suggested framework, SHAP was employed in order to clarify the significance of the attributes on the anticipated output. The proposed framework was trained with the Adam optimizer having a learning rate of 0.001, which adjusts the learning rates during training and enhances the convergence stability.