## 5 Experimental Results and Analysis
### Experimental Results
Each time, the communication terminal only performs one round of training for the representation learning on the attributed heterogeneous network, and a total of 20 communication rounds are performed. We select five users, 10 users, and 15 users, respectively, for the training, and conduct the prediction on a common group of five users. It can be seen from the figure that there is basically no difference in the accuracy of training with different numbers of users. This is because, in the FL-AHNEAP method, only the shared model is used to train the representation learning on the attributed heterogeneous network, whereas the link prediction model based on the neural network is used to realize the personalized prediction of users. Figure 4: Effect of communication rounds on FL-AHNEAP. Figure 5: Effect of terminal communication rounds on FL-AHNEAP. **Effect of the number of applications used by users on the accuracy of prediction:** Figure 7 describes the effect of the number of applications used in each user's training set on the accuracy of prediction. The FL-AHNEAP method uses 10 users for 20 rounds of communication, and each user is trained for five rounds at the terminal. It shows that for the three users B07, A11, and D00 with high prediction accuracy, the number of applications used in the training set is relatively small, whereas for B08 with the lowest prediction accuracy, the number of applications used in the training set is the largest. It can be basically inferred that the number of applications used by users has a certain impact on the prediction accuracy, and further research can be undertaken in the future. **Analysis of cold start of new users:** In the FL-AHNEAP method, each user can obtain a shared model, that is, a representation learning model for the attributed heterogeneous network. Therefore, for the cold start problem of new users, we can calculate the embedding of each new code based on the shared model, then calculate the cosine similarity of the node embedding, and select the node with higher similarity as the prediction result. Figure 8 shows the prediction accuracy results of the shared model (obtained after training based on the data of ten users: A01, B07, A11, D00, A03, A12, B06, B08, A04, and A07) to five users: D03, A06, A02, B03, and D04, who have not participated in the training. If a new user does not have a historical record at all, we can only make predictions based on the current time and location information. It can be seen that, except for D03, the prediction accuracy obtained by time and location information is almost the same, and all accuracies are higher than 20%. If a new user has some historical records, the prediction accuracy of time, location, and previous App can be considered at the same time. It can be seen that the prediction accuracy of the proposed FL-AHNEAP method is much higher than 20%. Figure 6: Effect of the number of users on FL-AHNEAP. Figure 7: Effected of the number of used applications on FL-AHNEAP. that the prediction accuracies based on the previous App are much higher than that of time and location information, and all are higher than 50%. User D03 achieves the highest result, reaching 71%, whereas user D04 is the lowest, but also has an accuracy of 51%. In actual situations, it is impossible for a new user to have no App usage record at all, so the candidate App set can be obtained by selecting the previous App information. **Effect of the number of user historical records on FL-AHNEAP:** Figure 9 shows the effect of the number of user historical records on the FL-AHNEAP method. One month, three month, and six month data of five users are selected for training, including 80% for the training set and 20% for the test set. The figure shows that the number of user historical records has little effect on the FL-AHNEAP method, and the fluctuation of the prediction accuracy of each user is small. When only one month's data of each user is selected for the training, the accuracy tends to be slightly higher.