## 12 Simulation Setup
However, the difference in convergence speed was marginal, indicating  
that FL can still achieve satisfactory convergence rates. In Figure 2, the proposed model provides a comparative analysis. The FL model incurred a higher communication overhead due to the need for frequent model synchronization between clients and the central server. This overhead was observed in terms of the number of rounds required for convergence and the amount of data transferred during each communication round. In contrast, the NFL model had a lower communication overhead, as all data reside within the central server. The FL model demonstrated improved robustness against individual client failures or dropouts. In the event of a client device becoming unavailable, the FL model was able to continue training using the remaining clients. On the other hand, the NFL model heavily relies on the central server and is more susceptible to disruptions caused by server failures. Based on the comparative analysis of the proposed FL model and NFL model, it can be concluded that FL offers several advantages, including preserving data privacy, enabling collaborative learning, and enhancing model robustness. Although FL incurs a higher communication overhead and slightly slower convergence speed compared to the NFL model, these trade-offs can be mitigated by optimizing communication protocols and leveraging advancements in federated learning techniques. The simulation results presented in Figure 2 provide valuable insights for researchers and practitioners to make informed decisions while choosing between FL and NFL approaches for distributed learning scenarios. Figure 3 provides a comparative study of the suggested federated learning model and the non-federated learning model [18] based on their resistance to attack and success rate of attacks, respectively. The purpose of this conversation is to investigate how resistant these models are to being attacked and how susceptible they are to being successfully attacked. Figure 3 presents a comparative study of attack resistance and the success rate of attacks. 1. The Federated Learning Model: When compared to non-federated learning systems, the federated learning model is designed to provide higher levels of security and privacy for its users. In this section, we conduct an analysis of the federated learning model's ability to withstand attacks and its success rate under assault, taking into account the following factors:  
* Resilience to Assaults: Due to the decentralized nature of federated learning models, these types of models exhibit improved resilience to many types of assaults. Because the training process takes place locally on each device, it is  
Figure 2: Comparative analysis of the proposed federated learning model and non-federated learning model. more difficult for adversaries to target a centralized server and compromise the entire model. * Success Rate of Attacks: The success rate of attacks is relatively lower in federated learning, since the model updates are aggregated from several devices. This makes it difficult for an attacker to manipulate the learning process. In addition, the local data privacy that is preserved by federated learning creates an additional barrier for the attacker to overcome in their quest to get sensitive information. 2. The Non-Federated Learning Model: Non-federated learning models adhere to the standard centralized method, in which all of the data and model parameters are kept on a single server. Let us have a look at the level of resistance to attacks, as well as the success rate of attacks in this context: * Resistance to Attacks: Non-federated learning models, which are centralized in nature, are more vulnerable to attacks than federated learning models. The centralized server can be a target for adversaries, which can result in the entire dataset, as well as model parameters, being compromised. This could then lead to unauthorized access or model poisoning.