# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 4 Experimental Results
### Correlation against the Benchmarks  
To demonstrate the efficacy of the AL techniques, Tables 6 and 7 show comparative results from using AL approaches to the two baselines in FL and a centralized learning context. For human sentiments in disaster situation datasets in federated and centralized learning, the model trained on the training samples labeled using the AL techniques and the manually labeled training sets yielded equivalent results. Nevertheless, the test accuracy remained higher than that indicated when trained models used training sets with AL techniques, which demonstrates the efficacy of AL techniques. To understand the variations in the performance of the models in centralized and FL, we compare the standard deviation values. The lower scores in the standard deviation of the performance parameters using individual AL methods in centralized and FL environments indicate that FL has the potential to attain comparable accuracy with enhanced privacy. There is a significantly greater difference between the performance of Criteria 1 and 2 and the suggested approach. Criteria 2 is the primary contributor to performance variance, because its results are lower in performance than those of Criteria 1 and the AL techniques. Moreover, we compared our results with recent work that is conducted in AL-based FL environments, which are presented in Table 9. [PERSON] et al. [43] conducted their experiments on the MOA airlines dataset [44] and proposed a communication-efficient distributed learning approach based on active federated learning. They achieved a test accuracy of 61% with 10 participating clients and 30 communication rounds. [PERSON] et al. [33] presented a similar approach to our approach where they used pre-trained ResNet [45] for feature extraction from two datasets. They analyzed five different active learning techniques in the federated learning environment. In their investigations, the performance of the techniques under different sampling and disagreement approaches varied significantly beyond the first \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{2}{c}{**Centralized Learning**} & \multicolumn{2}{c}{**FL**} & \multicolumn{2}{c}{**Standard Deviation**} \\  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Criteria 1 & 0.731 & 0.742 & 0.710 & 0.724 & 0.096268 & 0.097621 \\ \hline Criteria 2 & 0.698 & 0.679 & 0.685 & 0.681 & 0.114799 & 0.120991 \\ \hline Proposed Approach & 0.885 & 0.886 & 0.895 & 0.893 & 0.007071 & 0.00495 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy and F1-score assessment versus the two starting points. Baseline 2 is training data containing unrelated images, Criteria 1 (manual label) is the best scenario, where each instance is carefully tagged and evaluated when compared to Baseline 2 and Baseline 1 across both datasets. The suggested technique achieves a significant improvement in human sentiments in the disaster situation dataset context. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{2}{c}{**Centralized Learning**} & \multicolumn{2}{c}{**FL**} & \multicolumn{2}{c}{**Standard Deviation**} \\  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Criteria 1 & 0.811 & 0.802 & 0.710 & 0.724 & 0.091884 & 0.08817 \\ \hline Criteria 2 & 0.856 & 0.847 & 0.685 & 0.681 & 0.100254 & 0.100323 \\ \hline Proposed Approach & 0.891 & 0.888 & 0.899 & 0.896 & 0.005657 & 0.005657 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy and F1-score assessment versus the two starting points. Baseline 2 is training data containing unrelated images, Criteria 1 (manual label) is the best scenario, where each instance is carefully tagged and evaluated when compared to Baseline 2 and Baseline 1 across both datasets. The suggested technique achieves a significant improvement in human physical activities in the disaster situation dataset context. 500 iterations.