## 4 Experimental Evaluation and Results
### Results and Discussion  
The performance of the proposed framework for heart disease in terms of prediction accuracy, time to reach the accuracy, communication efficiency, and effect of local epoch on accuracy are measured and compared with state-of-the-art FedSGD, FedAvg, FedMA, and PSO optimizer with FedMA techniques. Figure 2 below shows the comparison of convergence rate with prediction accuracy on the heart disease dataset. The proposed framework achieves 92.89% accuracy on 3000 rounds of communication which is higher than the state-of-the-art FL and FedMA with PSO algorithms. Because our proposed framework utilizes the M-ABC optimizer for healthcare user sites and FedMA for the cloud model, this enables the model to achieve better accuracy faster than existing federated learning algorithms. In FedSGD, FedAvg, and FedMA, the cloud model tends to perform the simple gradient, averaging, and matched averaging, respectively, but their client model does not have any algorithm for feature selection and classification which results in higher convergence time for the cloud model. In PSO with FedMA, the learning rate is improved but the classification and feature selection consume higher convergence, whereas in our proposed framework the learning rate tends to increase faster after every round as compared with FedAvg and FedMA. Therefore, our proposed framework achieves higher accuracy in a lesser number of rounds. We have conducted experiments on the effect of local epochs on the accuracy as compared to state-of-the-art FedAvg and FedMA algorithms on the heart disease dataset. We considered the local epochs \(E\) to be as [10, 20, 40, 80, 100, 120, 140, 160]. For every \(E\), we \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation environment & Python \\ \hline Dataset utilized & UCI Cleveland \\ \hline Number of communication rounds & 4000 \\ \hline Local epochs & \{10, 20, 40, 80, 100, 120, 140, 160] \\ \hline Volume of communication (in GBs) & \{0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6] \\ \hline Number of client nodes & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters and settings. Figure 2: Comparison of communication efficiency. evaluated the accuracy test of the proposed framework, FedAvg, and FedMA. The result is illustrated in Figure 3 below. We observed that training our proposed framework for a longer time favors the convergence rate because our proposed framework returns a better global model on the local model with higher model quality as our proposed technique utilizes a modified-ABC optimizer. For FedSGD, FedAvg and FedMA, both did not employ any optimizer, so their accuracy tends to deteriorate as they train for a longer period but in the case of PSO with FedMA, the accuracy remains constant after 80 local epochs which is due to the slow convergence rate of PSO algorithm. This result depicts that user sites can use our proposed framework to continue training their model's local users for as long as they wish. We have evaluated and compared the performance of standard FL, PSO with FedMA and our proposed technique for the effect of prediction accuracy on the volume of communication. For this evaluation, we varied the volume of communication (in Gigabytes) as {0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6} and recorded the prediction accuracy of each technique as shown in Figure 4 above. It is observed from the results that the proposed technique achieves better accuracy at both low and high volumes of communication as compared to standard FL and PSO with FedMA. Moreover, in Figure 5 below a comparison of the size of communication used to reach 90% prediction accuracy is illustrated. The proposed technique uses 20% less communication size (in GB) as compared to existing federated learning algorithms. Figure 3: Effect of local epoch on accuracy.