## 12 Simulation Setup
This is due to the decentralized nature of the learning process, as each device or server performs local updates before aggregating them. However, the number of iterations can be reduced by employing advanced aggregation techniques and adaptive learning algorithms. 2. Time per Iteration: The time taken per iteration in federated learning depends on various factors, including the communication overhead between devices, the computational capabilities of the devices, and the complexity of the model. Since communication between devices is required during the aggregation process, the time per iteration in federated learning can be longer compared to non-federated learning, especially in scenarios with a large number of participating devices. 3. Non-Federated Learning Model: Non-federated learning models follow a centralized approach, where data and model parameters are stored and processed on a central server. Let us analyze the behavior of this model based on the number of iterations and the time taken per iteration: 1. Number of Iterations: Non-federated learning models typically require a smaller number of iterations to converge. Since the entire dataset is available centrally, the learning process can be more efficient and converge faster than federated learning models. 4. Time per Iteration: The time taken per iteration in non-federated learning is generally shorter compared to federated learning. This is because the training process is performed on a centralized server with access to the complete dataset, leading to faster computations and reduced communication overhead. The comparative analysis of the proposed federated learning model and non-federated learning model, considering the number of iterations and the time per iteration as illustrated through Figure 6, reveals the following insights:  
1. Convergence Speed: Non-federated learning models typically exhibit faster convergence due to their centralized nature, allowing access to the entire dataset during each iteration. Federated learning models may require a larger number of iterations to achieve convergence due to the decentralized training process and the need for model updates aggregation. 2. Computational Efficiency: Non-federated learning models often have a shorter time per iteration, as they operate on centralized servers and do not involve communication overhead between devices. Federated Learning models, on the other hand, may have longer time per iteration due to the need for communication and coordination among decentralized devices or servers. 3. Scalability Considerations: Federated learning models offer better scalability by allowing distributed training across numerous devices. This advantage comes at the cost of longer convergence times and increased communication overhead. Non-federatedlearning models might have faster convergence, but can face challenges when dealing with large-scale datasets and centralized processing requirements. The comparative analysis of Figure 6 highlights the trade-offs between the proposed federated learning model and non-federated learning model based on the number of iterations and time per iteration. Non-federated learning models tend to have faster convergence and lower time per iteration due to their centralized nature. Figure 7 presents a comparative analysis of the proposed federated learning model and non-federated learning model based on their vulnerability to attack scenarios and the corresponding attack success rates. This discussion aims to assess the robustness of these models against different attack scenarios and evaluate the likelihood of successful attacks. Figure 8 provides an analysis of the proposed federated learning model and non-federated learning model based on the number of iterations and time per iteration. This  
Figure 6: Comparative analysis of the proposed federated learning model and non-federated learning model based on the resistance to attack versus the number of transactions.