## 12 Simulation Setup
* Success Rate of Attacks: When compared to federated learning, the attack success rate in non-federated learning models may have a higher potential for success as illustrated through Figure 3 respectively. Attackers have a greater possibility of successfully manipulating the training process, inserting bad data, or exploiting flaws in the central server when they have access to centralized data and models. The following observations were made after conducting a comparative analysis between the federated learning model that was proposed and the non-federated learning model. The comparison focused on attack resistance and attack success rate. 1. Resistance to Attacks: The decentralized architecture of federated learning models makes them more resistant to attacks than centralized models. This makes the impact that focused attacks have on the overall system smaller. Non-federated learning models, on the other hand, are more susceptible to attacks from centralized locations, which can compromise both the dataset and the model as a whole. 2. A lower attack success rate is usual in federated learning models because of the distributed nature of the learning. Attackers have substantial challenges due to the distributed nature of federated learning and the maintenance of local data privacy,  
Figure 3: Comparative analysis of the proposed federated learning model and non-federated learning model. which reduces their capacity to successfully alter the training process. On the other hand, non-federated learning models have a larger risk of successful attacks due to the fact that centralized data storage and accessibility are their primary characteristics. The findings of the comparative analysis of attack resistance and attack success rate in the proposed federated learning model and the non-federated learning model illustrate the better security and privacy advantages of federated learning, as shown through Figure 3. In comparison to non-federated learning models, federated learning displays stronger resistance to attacks and lower success rates for attacks as a result of decentralizing the training process and maintaining local data privacy. These findings highlight how important it is to utilize federated learning methodologies in order to improve the privacy and security of machine learning systems. Figure 4 presents the simulation results based on comparative analysis of the proposed federated learning model and non-federated learning model based on the number of transactions and the time taken by each transaction. A comparison of the proposed federated learning (FL) model and the non-federated learning (NFL) model is shown in the figure below. The comparison is based on the models' ability to withstand attacks across a range of transaction counts. 1. _Defense Against Attacks_
2. Federated Learning Model 1. Decentralizing the learning process is one of the primary tenets of the federated learning model, which aims to improve both security and privacy. Evaluation of the model's susceptibility to attacks becomes possible as the number of transactions that occur increases. Federated learning makes use of a number of various strategies, including encryption, secure aggregation, and differential privacy methods, in order to increase resistance. Because of this, improved security against attacks aimed at data leakage, model poisoning, or inference attacks is enabled. 3. The Non-Federated Learning Model: Non-federated learning models have a centralized approach, meaning that all of the data and model parameters are saved on a single server. The security measures that are put into place in this model have the potential to have an effect on how resistant it is to attacks. Access control, encryption, and the detection of intrusions are all examples of standard security measures. On the  
Figure 4: Comparative analysis of the proposed federated learning model and non-federated learning model based on the attack resistance and attack success rate.