# A Federated Incremental Learning Algorithm Based on Dual Attention Mechanism Mechanism
## 4 Experimental Analysis
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Increments/Test Classes** & **Model** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{CIFAR10} & \multirow{2}{*}{2/10} & \multirow{2}{*}{Icarl-FedAvg} & 42.14 \\ \cline{3-4}  & & & Incre-FL & **48.01** \\ \cline{3-4}  & & & Learl-FedAvg & 61.49 \\ \cline{3-4}  & & & Incre-FL & **65.11** \\ \hline \multirow{4}{*}{CIFAR100} & \multirow{2}{*}{10/100} & \multirow{2}{*}{Icarl-FedAvg} & 29.44 \\ \cline{3-4}  & & & Incre-FL & **32.22** \\ \cline{3-4}  & & & Learl-FedAvg & 38.65 \\ \cline{3-4}  & & & Incre-FL & **40.62** \\ \cline{3-4}  & & & Learl-FedAvg & 38.42 \\ \cline{3-4}  & & & Incre-FL & **41.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The influence of the dual attention mechanism module. Figure 10: The accuracy curve of the dual attention mechanism module in the test set Cifar100. (**a**) Accuracy graph when the class increment is 10. (**b**) Accuracy graph when the class increment is 20. (**c**) Accuracy graph when the class increment is 50. Figures 13 and 14 show the test results of the Incre-FL algorithm and the Icral-FedAvg algorithm with the feature attention mechanism added separately on the CIFAR10 and CIFAR100 standard test sets. It can be seen from the figure that the accuracy of the algorithm in this paper is significantly better than that of the Earl-FedAvg algorithm. The algorithm's accuracy in this paper on the CIFAR10 dataset when the classification increments are 2 and 5 reaches 45.40% and 63.73%. On the CIFAR100 dataset, the accuracy rates when the classification increments are 10, 20, and 50 reach 31.02%, 39.86%, and 40.32%. Figure 11: The accuracy curve of the channel attention mechanism module in the test set Cifar10. (**a**) Accuracy graph when the class increment is 2. (**b**) Accuracy graph when the class increment is 5. Figure 12: The accuracy curve of the channel attention mechanism module in the test set Cifar100. (**a**) Accuracy graph when the class increment is 10. (**b**) Accuracy graph when the class increment is 20. (**c**) Accuracy graph when the class increment is 50. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Increments/Test Classes** & **Model** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{CIFAR10} & \multirow{2}{*}{2/10} & Icral-FedAvg & 42.14 \\ \cline{3-4}  & & & Incre-FL & **47.53** \\ \cline{3-4}  & & & Icral-FedAvg & 61.49 \\ \cline{3-4}  & & & Incre-FL & **64.47** \\ \hline \multirow{4}{*}{CIFAR100} & \multirow{2}{*}{10/100} & Icral-FedAvg & 29.44 \\ \cline{3-4}  & & & Incre-FL & **32.09** \\ \cline{3-4}  & & & Icral-FedAvg & 38.65 \\ \cline{3-4}  & & & Incre-FL & **40.20** \\ \cline{3-4}  & & & Icral-FedAvg & 38.42 \\ \cline{3-4}  & & & Incre-FL & **41.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The influence of the dual attention mechanism module. The influence of the feature attention mechanism in this paper is shown in Table 4. It can be seen from the table that adding the feature space attention mechanism can improve the accuracy of image classification tasks in incremental learning by enhancing the capture performance of crucial information. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Increments/Test Classes** & **Model** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{CIFAR10} & \multirow{2}{*}{2/10} & \multirow{2}{*}{Icarl-FedAvg} & 42.14 \\ \cline{3-4}  & & & Incre-FL & **45.40** \\ \cline{3-4}  & & & Learl-FedAvg & 61.49 \\ \cline{3-4}  & & & Incre-FL & **63.73** \\ \hline \multirow{4}{*}{CIFAR100} & \multirow{2}{*}{10/100} & \multirow{2}{*}{Icarl-FedAvg} & 29.44 \\ \cline{3-4}  & & & Incre-FL & **31.02** \\ \cline{3-4}  & & & Incre-FL & **39.86** \\ \cline{3-4}  & & & Incre-FL & **40.32** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence of the dual attention mechanism module. Figure 14: The accuracy curve of the feature attention mechanism module in the test set Cifar100. (**a**) Accuracy graph when the class increment is 10. (**b**) Accuracy graph when the class increment is 20.