## 5 Experimental Evaluation and Validation
### Results and Discussion
Figure 4 shows the outcome of this test. The findings show that the suggested framework can train for a longer period and supports a higher rate of convergence because it produces a better HSP global model on the local model with a higher model quality. This is because our suggested technique makes use of the RB-SVM and MABC methods at the client side of HSP. The accuracy of traditional FL algorithms, such as FedAvg and FedMA, tends to decrease with time due to the lack of an optimizer. However, in the case of the GA-SVM with FedMA, the accuracy does not decrease much after 100 local epochs, which is attributable to the GA algorithm. This result demonstrates that if user sites implement our recommended structure, they are free to train the local users of their model indefinitely. Figure 3: Comparison of convergence rate with prediction accuracy. For the influence of prediction accuracy on the utilized communication volume, we examine and compare the performances of the FedAvg-SVM, Fed-MA-SVM, GA-SVM with FedMA, and our suggested approach. We vary the volume of communication (in Gigabytes) for this assessment as {0.6, 1.2, 1.8, 2.4, 3.0, 3.6, 4.2, 4.8, 5.4, 6.0} and record the prediction accuracy of each approach, as shown in Figure 5. The recorded results show that our hybrid approach outperforms traditional FL techniques and FedMA with the GA-SVM in terms of accuracy at both low and large communication volumes. Furthermore, Figure 6 depicts a comparison of the extent of communication necessary to achieve the various target prediction accuracy of the algorithms (70%, 75%, 80%, 85%, and 90%). The GA-SVM outperforms our suggested approach for a lower target accuracy of 70% and 75%. However, when compared to existing FL algorithms, our proposed approach consumes 15-25% less communication size (in GB) for improved target accuracy. Figure 4: Comparison of the effect of local epoch on the prediction accuracy. Figure 5: Analysis of prediction accuracy on the volume of communication. Performance metrics such as accuracy, precision, classification error, f-measure, specificity, sensitivity, and the number of rounds required to achieve the highest accuracy are assessed for the performance efficiency comparison of the proposed framework with the FedAvg-SVM, FedMA-SVM, and GA-SVM with FedMA. Accuracy in machine learning refers to the proportion of all available examples that yield the right predictions. The fraction of accurate positive instance predictions is what is referred to as precision. Classification errors are defined as the inaccuracies or proportions of mistakes that are readily available in the instance. Three performance indicators are used to identify important heart disease symptoms. This makes it easier to comprehend how different groups behave and enables better feature selection. The results of these parameters are displayed in Tables 3 and 4. The created GA-SVM with FedMA for the heart disease dataset and baseline FL approaches are compared to our proposed framework, which achieves greater target accuracy in less cycles. The number of rounds in our suggested technique is reduced by 37% when compared to existing methods, as shown in Table 3, since our proposed model's learning rate grows quickly after each round, leading to fewer rounds. Table 3 demonstrates that the proposed framework performs better on the heart disease dataset than FL state-of-the-art methods in terms of prediction accuracy (93.8%), precision (94.2%), sensitivity (96.6), and specificity (81.8), because of the proposed model's improved learning rate that increases with each communication round with a smaller minibatch size. Our proposed framework is therefore more equipped to provide increased heart disease prediction accuracy while maintaining privacy when compared to existing baseline FL techniques. In addition, the MABC optimization technique for feature selection and the RB-SVM classification in our proposed framework result in decreased classification errors, resulting in a classification error of 11.9 for our recommended method.