# A Federated Incremental Learning Algorithm Based on Dual Attention Mechanism Mechanism
## 4 Experimental Analysis
### Ablation Experiment  
_Rationality Analysis of Dual Attention Mechanism Module_  
There is a problem of sample imbalance in each client in federated learning. A client with a large sample size has a significant weight parameter, which significantly impacts the final model training result. At the same time, federated learning adds incremental learning for dynamic task training and learns at the same time in classification increments. When the sample importance is unbalanced, the matter is challenging to capture. This experiment is an ablation experiment. To use only the dual attention mechanism method of innovation 2, without using the pre-training module, it is compared with the Icarl-FedAvg learning algorithm to verify the effect of the dual attention mechanism on the accuracy improvement. Figures 9 and 10 show the accuracy curves of the dual attention mechanism module in test set Cifar10 and Cifar100. The influence of the dual attention mechanism module in this paper is shown in Table 2. In the improved Incre-FL algorithm in this paper, the channel attention mechanism is added to the client; we can obtain the characteristics of the overall samples of each client, reduce the influence of noise, and add a feature space attention mechanism during federated aggregation, which can enhance the global model. The capture performance of the client's critical information will ultimately improve the accuracy of the image classification task. The dual attention mechanism in the experiment of this chapter includes the channel attention mechanism and the feature attention mechanism. Next, we verify the accuracy improvement effect of adding the channel attention mechanism and the feature attention mechanism separately. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Increments/Test Classes** & **Model** & **Accuracy (\%)** \\ \hline \multirow{6}{*}{CIFAR100} & \multirow{2}{*}{10/100} & Icarl-FedAvg & 29.44 \\ \cline{3-4}  & & Incre-FL & **30.03** \\ \cline{3-4}  & & Icarl-FedAvg & 38.65 \\ \cline{3-4}  & & Incre-FL & **39.35** \\ \cline{3-4}  & & Icarl-FedAvg & 38.42 \\ \cline{3-4}  & & Incre-FL & **39.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 9: The accuracy curve of the dual 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. Figures 11 and 12 show the test results of the Incre-FL algorithm, the Icarl-FedAvg algorithm, the SE-Icarl algorithm, and the Earl algorithm with the channel 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 the Icarl-FedAvg algorithm, and the SE-Icarl algorithm with the channel attention mechanism alone is better than the Icarl algorithm. The algorithm's accuracy in this paper on the CIFAR10 dataset is 47.53% and 64.47% when the classification increments are 2 and 5. The accuracies on the CIFAR100 dataset with classification increments of 10, 20, and 50 reach 32.09%, 40.20%, and 41.23%.The influence of the channel attention mechanism in this paper is shown in Table 3. The channel attention mechanism can model the dependencies between channels in the external network, help to focus on the importance of the overall features of the samples, and reduce the impact of noise on the final model training results. We add it to the Earl algorithm and the Icarl-FedAvg algorithm to improve the accuracy of image classification tasks.