## 12 Simulation Setup
other hand, due to the fact that they store data in a centralized location and may have a single point of failure, centralized models are often more prone to being attacked. _2. Number of Transactions_  
The term \"number of transactions\" refers to the total number of operations or interactions that are carried out inside of the learning system. It can describe how often data are updated, how often models are updated, or how often devices or servers communicate with one another. _3. Comparative Analysis_  
Figure 5 provides insight into how well the federated learning model and the non-federated learning model perform in terms of security and resistance by assessing the resistance to assaults versus the number of transactions. This analysis can be found in the Comparative Analysis section. It is possible to make the following observations:  
* The Federated Learning Model: The federated learning model displays its resistance against attacks as the number of transactions increases. Federated learning, because of its decentralized structure and the tactics it uses to protect users' privacy, helps reduce the likelihood of malicious attacks and the exposure of sensitive data. The capability of the model to divide the learning process across numerous devices or servers improves its resilience to attacks, making it an appealing choice for privacy-sensitive applications because it increases the model's overall robustness. * The Non-Federated Learning Model: The resistance to attacks of the non-federated learning model may be modified by the amount of transactions, but it generally remains lower than that of the federated learning model. The fact that the processing and storage are centralized makes it more susceptible to assault, which is especially true as the volume of transactions rises. As the central server processes a greater volume of data and transactions, it opens itself up to becoming a potential target for attackers, which, in turn, increases the likelihood that an assault would be successful. Figure 5's comparative analysis demonstrates the advantages of the federated learning model over the non-federated learning model in terms of resistance to assaults as the number of transactions increases. These advantages are highlighted in the conclusion of this analysis. Federated learning helps protect against a variety of assaults thanks to the fact that it is decentralized and protects users' privacy at the same time. This provides increased security and privacy assurances. On the other hand, due to the fact that non-federated learning models are centralized, they are more susceptible to being attacked, particularly  
Figure 5: Comparative analysis of the proposed federated learning model and non-federated learning model based on number of transactions and the time taken by each transaction. when the total number of transactions increases. These findings highlight how important it is to take into consideration federated learning for applications that place a high priority on security and privacy in situations that involve a significant number of transactions [45]. Figure 6 presents the simulation results based on federated learning model and non federated learning model based on the number of iterations and time per iterations. Moreover, Figure 6 presents a comparative analysis of the proposed federated learning (FL) model and non-federated learning (NFL) model based on the number of iterations and the time taken per iteration. This discussion aims to explore the trade-offs between the two models in terms of convergence speed and computational efficiency [46]. Figure 6 is completely explained here:  
1. Federated Learning Model: Federated learning allows training machine learning models on decentralized devices while preserving data privacy. Let us examine the model's behavior in terms of the number of iterations and the time taken per iteration: 1. Number of Iterations: Federated learning models might require a larger number of iterations to converge compared to non-federated Learning models.