## 12 Simulation Setup
The federated learning model's ability to distribute computation and lessen the load on individual devices might be evident from a more stable resource utilization pattern. The analysis should be considered in the context of real-world applications. While the federated learning model's performance metrics might appear favorable in the controlled environment of the analysis, real-world deployment challenges such as communication delays, device heterogeneity, and data privacy concerns need to be factored in when making decisions about the feasibility and advantages of each approach. In conclusion, the analysis in Figure 8 of the proposed federated learning model and non-federated learning model based on the number of iterations and time per iteration is a crucial step in understanding the relative strengths and weaknesses of these approaches. It provides insights into their convergence rates, time efficiency, scalability, and resource utilization, aiding in making informed decisions about the choice of training methodology based on the specific requirements and constraints of the problem at hand. The comparison highlights a trade-off between privacy and accuracy through Figure 1:3 While federated learning protects individual data privacy, it might lead to a slight reduction in model accuracy. Non-federated learning, on the other hand, offers higher accuracy but at the expense of centralized data vulnerability. The findings have implications for choosing the appropriate model for different deployment scenarios. Depending on the specific application and the importance of data privacy and model accuracy, stakeholders may lean toward one model over the other. The Need for Tailored Solutions: The analysis underscores the importance of tailoring security solutions based on the specific attack landscape and model architecture. An effective defense strategy might involve combining techniques from both federated and non-federated learning to mitigate different types of attacks. Figure 1:4 Comparative analysis of the proposed federated learning model and non-federated learning model based on the attack scenarios and attack success rate. The analysis prompts further exploration into refining both federated and non-federated learning models. Future research could focus on enhancing the privacy mechanisms of federated learning or devising methods to fortify the security of non-federated models. In conclusion, the comparative analysis in Figure 9 offers a nuanced understanding of how the proposed federated learning model and the traditional non-federated learning model perform in the face of various attack scenarios. It highlights the trade-offs between data privacy and model accuracy while emphasizing the need for context-specific security strategies in machine learning deployment. **1. Federated Learning Model:** Federated learning models are designed to address security and privacy concerns in distributed machine learning settings. The federated learning model's vulnerability to attack scenarios and the corresponding attack success rate are clearly illustrated through Figure 8's simulation results and we have explained it in the following sections:  
Attack ScenariosFederated learning models can be susceptible to certain attack scenarios, including federated poisoning attacks, Byzantine attacks, and model inversion attacks. These scenarios exploit vulnerabilities in the communication process, model aggregation, or model updates. Attack Success RateThe attack success rate in federated learning models depends on various factors, such as the robustness of the model aggregation mechanism, the detection and mitigation techniques employed, and the level of communication security. A well-implemented federated learning system can effectively minimize the attack success rate by incorporating appropriate security measures. **2. Non-Federated Learning Model:** Non-federated learning models follow the traditional centralized approach, where data and model parameters are stored in a central server.