## 12 Simulation Setup
makes it possible to customize the experience based on the preferences of each individual user while still maintaining their privacy. 2. The Non-Federated Learning Model: The non-federated learning model refers to classic centralized machine learning systems in which data are collected and stored on a central server or in a cloud environment. In this particular model, the training data are readily available for access by the centralized server, and the model itself is educated with the assistance of the centralized dataset. The non-federated learning model has a number of essential qualities, including the following: * Data Storage in a Central Location: When using a method that is not federated, all of the training data are amassed and kept on a single server. The training process is made more straightforward as a result of this centralization, which enables direct access to the entire dataset. * Less Privacy: Due to the centralized nature of the training data, there is a greater possibility of privacy being compromised. When working with sensitive information, protecting the privacy of one's data becomes a more important concern because it becomes a single point of vulnerability. * Scalability issues: Centralized learning models encounter scalability issues as the amount of data they need to learn from grows. Because the central server must process enormous amounts of data, there will be an increase in the amount of overhead caused by computing and communication. The following are some of the key conclusions from an investigation into the similarities and differences between the non-federated learning model and the federated learning model that has been proposed:  
1. Privacy: Federated learning is excellent at safeguarding users' privacy, since it keeps raw data spread across multiple devices, hence lowering the likelihood of data being exposed to unauthorized parties. The non-federated approach, on the other hand, raises concerns with regard to data privacy because of the centralized nature of the data storage. 2. Scalability: Because the learning process is carried out simultaneously on a number of different devices, federated learning provides inherent advantages in terms of scalability. When dealing with huge datasets and the requirement for centralized processing, non-federated learning, on the other hand, confronts scalability challenges. 3. The Availability of Data: In the non-federated method, all of the training data are stored in one location, making it possible to perform a complete analysis. When using federated learning, each device is only responsible for a subset of the data, which restricts the overall view of the dataset. 4. The Burden of Communication: Federated learning necessitates communication between devices and the central server/coordinator throughout the process of model aggregation. This results in an additional burden of communication. This communication overhead can have an impact on training efficiency; however, non-federated learning eliminates this overhead entirely. The comparative study of the suggested federated learning model and the non-federated learning model sheds light on the trade-offs, including the availability of data, scalability, and privacy concerns. The use of federated learning offers a strategy that protects users' privacy, as shown through Figure 1.  
Figure 2 presents the simulation results based on the comparative analysis of the proposed FL model and NFL model. The following key findings were observed: The FL model achieved comparable training accuracy to the NFL model [18]. This demonstrates that FL can effectively leverage the collective knowledge of client devices without compromising accuracy, despite the inherent challenges of decentralized learning. The FL model exhibited slightly slower convergence compared to the NFL model. This can be attributed to the communication overhead involved in synchronizing the local model updates across multiple client devices.