## 4 Evaluation Parameters  
The performance evaluation of the proposed model in mainly divided into three parts, (i) local training performance, (ii) federated server performance, and (iii) statistical performance. For the local training performance, the performance parameters are the regression value and the Mean Square Error (MSE) value. For federated servers, the performance parameters are the classification accuracy (CA) and misclassification rate (MCR), which can be stated in the confusion matrix form. The statistical performances are based on the outcomes of the confusion matrix, i.e., the positive ratio, negative ratio, false positive, and false negative values. These statistical performances include classification accuracy (CA), misclassification rate (MCR), sensitivity, specificity, F1-Score, Negative Partial Value (NPV), False Positive Ratio (FPR), and False Negative Ratio (FNR). The mathematical form of the statistical parameters is given below in Equations (21)-(28).  
Classification accuracy (\(CA\)) is the measure of the correctly predicted instances for each class in the prediction of machine learning. The higher values of classification accuracy mean better training in machine learning.  
\[CA=\frac{PR+NR}{PR+NR+FP+FN}*100 \tag{21}\]  
The misclassification rate (\(MCR\)) is the value of wrongly predicted instances by machine learning and should be as low as possible.  
\[MCR=100-\frac{PR+NR}{PR+NR+FP+FN}*100 \tag{22}\]  
Sensitivity is a measure of how much a system can predict true instances. This is the ability of any machine learning model to correctly identify the true instances, and this is also known as the true positive rate (\(TPR\)).  
\[Sensitivity=\frac{PR}{PR+FN}*100 \tag{23}\]  
Specificity measures the proportion of the true negatives correctly identified by the model. This is also known as the true negative rate (\(TNR\)). This parameter should be of higher value for a good machine-learning model.  
\[Specificity=\frac{NR}{NR+FP}*100 \tag{24}\]  
\(F1\)-\(score\) is the combination and harmonic mean of precision and recall. This metric includes both the precision and recall factor of machine learning. For a higher \(F1\)-\(Score\), the precision and recall metric of the machine learning model should be high.  
\[F1\text{-}score=\frac{2 PR}{2 PR+FP+FN}*100 \tag{25}\]  
Negative partial value (\(NPV\)) is the ability of machine learning to predict the negative instance as negative, not predicting the negative instances as the positive instance. The higher \(NPV\) means a higher stable machine learning model.  
\[NPV=\frac{NR}{NR+FN}*100 \tag{26}\]  
False positive rate (\(FPR\)) is the measure of the false positive predictions by machine learning against all positive predictions. The smaller value of (\(FPR\)) means, the higher the classification accuracy of the model. The sum of the specificity and (\(FPR\)) should be 1.  
\[FPR=100-\frac{NR}{NR+FN}*100 \tag{27}\]  
False negative rate (\(FNR\)) is the measure of the incorrectly predicted negative instances. This metric should be of low value for a better machine-learning model. The sum of sensitivity and \(FNR\) should be 1.  
\[FNR=100-\frac{PR}{PR+FN}*100 \tag{28}\]