## 12 Simulation Setup
Figure 7: Comparative analysis of the proposed federated learning model and non-federated learning model based on the number of iterations and time per iteration. analysis provides valuable insights into the performance and efficiency of the two approaches and aids in understanding their respective strengths and limitations. The horizontal axis of the figure represents the number of iterations, indicating the progression of training over time. The vertical axis represents the time taken for each iteration, illustrating the computational cost associated with each iteration of the training process. The graph's trend lines for both the federated learning and non-federated learning models reveal insights into their convergence speeds, which are clearly shown through Figure 1:0 A faster convergence speed in fewer iterations might indicate the efficiency of the training approach. If the federated learning model reaches a comparable level of accuracy or loss with fewer iterations, it could demonstrate the advantage of leveraging distributed data and collaborative learning. Comparing the time per iteration between the two models is crucial. If the federated learning model exhibits significantly lower time per iteration while maintaining a comparable or better convergence rate, it suggests that the federated approach is more time-efficient due to the parallelized training across multiple devices as shown in Figure 1:1  
Figure 9 provides a comparative analysis of the proposed federated learning model and non-federated learning model based on the attack scenarios and attack success rate. The comparative analysis presented in Figure 9 demonstrates the contrasting performance of a proposed federated learning model and a traditional non-federated learning model in the face of various attack scenarios. Across multiple attack scenarios, the success rates of attacks on both the federated and non-federated learning models vary significantly. Certain attack types may have higher success rates in one model compared to the other. This discrepancy is indicative of the influence of the model's architecture and data distribution. In some attack scenarios, the federated learning model showcases a heightened robustness. It exhibits lower attack success rates compared to the non-federated model. This suggests that the collaborative nature of federated learning, where data remain decentralized, may contribute to an increased security against certain attacks. Conversely, the non-federated learning model might show greater vulnerability to specific attack types. This could stem from the centralization of data, making it a more attractive target for certain attacks. The attack success rates in this model might be higher due to the concentrated dataset. Figure 1:2 Comparative analysis of the proposed federated learning model and non-federated learning model based on number of iterations and time per iteration. As the non-federated learning model's time per iteration increases substantially with the number of iterations, it might indicate limitations in scaling the approach to larger datasets. On the other hand, if the federated learning model maintains a relatively consistent time per iteration despite increasing iterations, it could showcase its ability to handle larger datasets through distributed learning. The analysis could highlight potential trade-offs between the two approaches. While federated learning might show advantages in terms of time per iteration and scalability, the non-federated learning model could excel in certain scenarios where communication overhead and device heterogeneity impact the federated learning's efficiency. The graph could provide insights into the resource utilization patterns of both models. If the non-federated learning model exhibits high resource consumption with increasing iterations, it could indicate inefficiencies in terms of memory or computational power.