## 5 Results and Discussion  
In the experiment, we use the logistic regression model to classify the students' academic performance in binary, the network structure is shown in Figure 1, and we use the federated transfer learning framework based on heterogeneous domain adaptation defined by this paper.  
In each scenario, we conducted three pieces of training, namely, training only for the source domain, training only for the target domain, and transfer training. We give the classification accuracy of the source domain and target domain under each training mode  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l} \hline \hline
**Variables** & **(I)** & **(Z)** & **(S)** & **(A)** & **(S)** & **(6)** & **(7)** & **(8)** & **(9)** & **(10)** & **(11)** & **(12)** & **(13)** & **(14)** & **(15)** & **(16)** \\ \hline \hline \multicolumn{12}{l}{(1) address} & \multicolumn{12}{l}{\(1\)} \\ (2) famsize & \(-0.104\) & \(1\)} & & & & & & & & & & & & & & & \\ (3) fedu & \(-0.009\) & \(0.112\) & \(1\) & & & & & & & & &  
and give the classification accuracy of the domain under the transfer training mode. The results are shown in Table 4.  
We can find that, in Scenarios 2-4, the results of target-only training are generally a little worse than source-only training due to the difference in the data size of the source and target domains and the different contributions of the included features to the principal components. Moreover, in Scenario 1, after using domain adaptation training, the accuracy results are not much different from the results of individual training in each domain, which indicates that using domain adaptation training will not improve the model effect in this case. In Scenario 2, after using domain adaptation, the classification accuracy of the target domain is greatly enhanced. In Scenario 3, both the source domain's classification accuracy and the target domain have dramatically increased. However, in Scenario 4, the classification accuracy of the source domain and the target domain increased less than that of Scenario 3.  
The result analysis shows that the domain adaptation model has little effect when the feature distribution and scale of the source domain and the target domain are consistent. However, when one party's data scale is small, it can benefit from federated learning. Especially when the feature distribution of both parties is inconsistent, both parties can learn new knowledge from each other and improve the classification accuracy. In Scenario 4, although we project the source and target domains into a common feature subspace, since the source and target domains have different feature distributions and data scales, the training knowledge on the source domain is directly applied to the target domain, and the resulting effect will be inferior to Scenario 2 and Scenario 3.  
From the experimental results, the federated transfer learning framework designed by us can effectively solve the problem of federated learning with small unilateral data and missing unilateral features in students' grades classification, and all participants can obtain new knowledge from each other while maintaining privacy. Experiments show that applying a federated learning framework can solve the problem of data islands in educational data mining, which can be used for reference in the field of intelligent education.  
The study has potential limitations. Since the experimental part of this paper only verifies the data exchange between two different education platforms, and the data differences generated by different education platforms may be large, how to transfer privacy protection knowledge between more online education platforms still needs further exploration.