## 12 Simulation Setup  
Table 1 presents the simulation setup and parameters used for evaluating the proposed approach. Table 2 provides details about the simulation setup and parameters used during the proposed experiment. The simulation was conducted with a duration of 1000 s, during which 1000 transactions were processed. A network latency of 5 ms was considered to simulate the communication delays. The block size was set to 1 MB, and the consensus algorithm employed was Proof-of-Work. The simulation setup and parameters outlined in Table 2 provide the foundation for evaluating the proposed approach. By adjusting these parameters, further \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Hardware** & **Software** \\ \hline Processor & Any modern multicore processor \\ \hline Memory & Minimum 8 GB RAM \\ \hline Storage & Sufficient disk space for dataset storage and simulation \\ \hline Operating System & Windows 10, macOS, or Linux \\ \hline Blockchain Framework & Ethereum, Hyperledger Fabric, or compatible blockchain framework \\ \hline Federated Learning Library & TensorFlow Federated, PySyft, or compatible federated learning library \\ \hline Data Privacy Techniques & Differential privacy, secure multi-party computation, or compatible privacy preservation techniques \\ \hline Simulation Environment & Python 3.x with required libraries (e.g., NumPy, Pandas) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware and software requirements. experiments can be conducted to analyze the system's performance and assess its suitability for specific use cases. In recent years, with the exponential expansion of data and the requirement for privacy-preserving machine learning, federated learning has emerged as a potential technique. This is due to the fact that federated learning allows for multiple users to share data. A non-federated learning model is contrasted with the federated learning model that was proposed in Figure 1, which presents a comparative study of the two models. The purpose of this debate is to examine the fundamental distinctions between these two techniques, as well as their respective benefits and drawbacks. The following is a description of Figure 1: A comparative analysis of the proposed federated learning model and the non-federated learning model. 1. The federated learning model allows machine learning models to be trained jointly across different decentralized devices or servers without sharing the raw data in Figure 1. This can be accomplished through federated learning. Instead, the models are trained locally on each device, and only the updates to the models themselves are accumulated and communicated with a central server or coordinator. The following is a list of major aspects that are included in the federated learning model:  
* Data privacy and security federated learning protects data privacy by storing sensitive information locally, where it may be accessed only by authorized users. The raw data are never sent off from the devices, which keeps the confidentiality of the information intact and lowers the likelihood of data breaches. * Decentralized Training: The federated learning architecture makes it possible to receive training on distributed devices, such as mobile phones and edge devices. Because of this decentralization, the reliance on a centralized server is reduced, which, in turn, enables efficient scalability and cuts down on communication overhead. * Individualized Model Updates: Federated learning allows for individualized model updates because the models are learned locally on each device. This \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation duration & 1000 s \\ Number of transactions & 1000 \\ Network latency & 5 ms \\ Block size & 1 MB \\ Consensus algorithm & Proof-of-Work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation setup and parameters. Figure 1: Proposed framework.