# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 4 Experimental Results
### Evaluation of the Impact on the Accuracy by the Number of Connected Nodes  
Figure 6a,b illustrate the impact of the number of connected nodes on the accuracy, where there is a trade-off between the accuracy obtained from the aggregator model and the number of connected nodes in the FL context. The primary objective of this test is to determine whether extensions of connected nodes affect the accuracy of the federated models. It was evaluated with two nodes by distributing the dataset and then incrementally starting the connection of other nodes to participate. By connecting more nodes, the efficiency of both datasets experienced a considerable decrease, as shown in Figure 6. Initially, when there were fewer nodes, the accuracy in both datasets was consistent. However, as the number of nodes increased, the accuracy decreased more quickly, leading to a substantial decline in the number of training samples for each node. The drop in the accuracy of human sentiments in disaster data is somewhat greater than that of human physical activities in natural disaster scenarios. This indicates that there were fewer training instances in the dataset. The evaluation leads us to perform another experiment, in which we assess the effect of increasing the training set at each client through AL on the accuracy.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**FL**} & \multicolumn{3}{c}{**Centralized Learning**} & \multicolumn{3}{c}{**Standard Deviation**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Vote entropy & 0.853 & 0.846 & 0.831 & 0.821 & 0.825 & 0.823 & 0.020076 & 0.018964 & 0.019547 \\ \hline Max disagreement & 0.882 & 0.881 & 0.879 & 0.876 & 0.881 & 0.860 & 0.013895 & 0.015545 & 0.017752 \\ \hline Margin sampling & 0.864 & 0.851 & 0.840 & 0.845 & 0.851 & 0.818 & 0.015285 & 0.015212 & 0.017532 \\ \hline Consensus entropy & 0.879 & 0.867 & 0.863 & 0.876 & 0.861 & 0.863 & 0.002121 & 0.018964 & 0.001414 \\ \hline Entropy sampling & 0.881 & 0.875 & 0.846 & 0.897 & 0.890 & 0.875 & 0.009394 & 0.012553 & 0.011899 \\ \hline Least confidence & 0.866 & 0.842 & 0.850 & 0.866 & 0.862 & 0.861 & 0.011502 & 0.015968 & 0.010289 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative assessments of the AL approach in FL (client nodes = 6) and centralized learning contexts with respect to the weighted accuracy, recall, and F1-score under various sampling and disagreement techniques for human physical activities in the disaster situation dataset context.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**FL**} & \multicolumn{3}{c}{**Centralized Learning**} & \multicolumn{3}{c}{**Standard Deviation**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Vote entropy & 0.834 & 0.806 & 0.824 & 0.832 & 0.821 & 0.824 & 0.019116 & 0.023016 & 0.022417 \\ \hline Max disagreement & 0.892 & 0.871 & 0.875 & 0.882 & 0.884 & 0.890 & 0.016384 & 0.018116 & 0.020189 \\ \hline Margin sampling & 0.848 & 0.823 & 0.830 & 0.835 & 0.833 & 0.822 & 0.012209 & 0.014696 & 0.016822 \\ \hline Consensus entropy & 0.874 & 0.865 & 0.870 & 0.856 & 0.861 & 0.852 & 0.009192 & 0.002828 & 0.006364 \\ \hline Entropy sampling & 0.859 & 0.851 & 0.855 & 0.867 & 0.860 & 0.865 & 0.006752 & 0.005909 & 0.006344 \\ \hline Least confidence & 0.866 & 0.842 & 0.850 & 0.856 & 0.852 & 0.856 & 0.006494 & 0.008424 & 0.007287 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative assessments of the AL approach in FL (client nodes = 6) and centralized learning contexts with respect to the weighted accuracy, recall, and F1-score under various sampling and disagreement techniques for human sentiments in the disaster situation dataset context.