## 5 Experimental Results and Analysis
### Experimental Results  
AHNEAP vs. FL-AHNEAP: Figure 3 compares the accuracy between the AHNEAP method and the FL-AHNEAP method. The FL-AHNEAP method uses 10 users for 20 rounds of communication, and each user is trained for five rounds at the terminal. As for the AHNEAP method, each user is trained for 100 rounds independently. FL-Strict strictly implements the FL-AHNEAP method, selecting 15 users for training and conducting 20 rounds of communication. The users are trained at the terminal for five rounds, and five users are randomly selected to participate in one round of communication according to a one-third ratio. It can be seen from Figure 3 that the experimental results of FL-AHNEAP and FL-Strict are not much different, but the accuracy of the two is generally lower than that of the AHNEAP method of single-user data. Among these, the accuracy of user D00 is the highest, reaching 72%, whereas user B08 has the lowest accuracy, of only 40%. For user A12, the accuracy of the FL-AHNEAP method is 17.5% lower than that of the AHNEAP method, which is the largest difference among all users, whereas the difference of user A03 is the smallest, at only 2.5% lower. This is because the FL-AHNEAP method destroys the structural property of the network to a certain extent; from the perspective of federated learning, it is equivalent to sacrificing part of the accuracy to obtain privacy protection. In the figure, the abscissa consists of 10 users arranged from left to right based on the number of historical records. From the results of AHNEAP, it is obvious that each user has different App usage patterns, the accuracy of App prediction based on AHNEAP method is different, and there is no direct relationship between the accuracy of prediction and the number of historical records. User A11 does not have the most historical records, but has the highest accuracy rate of 79%, whereas user B08 has significantly more historical records, but has the lowest accuracy rate of only 48%. **Effect of communication rounds on FL-AHNEAP:** Figure 4 compares the effect of communication rounds on FL-AHNEAP. Five users are selected to train the shared model. 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. It can be seen from the figure that the communication rounds have little effect on the prediction accuracy. For users D00, B07, and B08, there is almost no effect, whereas user A04 has a slight fluctuation, and A07 is relatively unstable. Generally speaking, however, the fluctuation range of prediction accuracy is not large; thus, in this case, it may only need one round of communication to obtain better results. Figure 3: AHNEAP vs. FL-AHNEAP. **Effect of terminal communication rounds on FL-AHNEAP:** Figure 5 compares the effect of terminal communication rounds on FL-AHNEAP. Ten users are selected to participate in training and 20 communication rounds are performed. Each time, the communication terminal performs one round, five rounds, and 10 rounds of training, respectively, for the representation learning on the attributed heterogeneous network. It can be seen from the figure that the prediction accuracy rates of 10 users in the three cases almost overlap. For users A01 and A07, the prediction accuracy rate of one round of training is slightly higher. In can be drawn from the experiment stated in Reference [2] that the AHNEAP method can converge quickly, so it can be inferred that the terminal training round has little effect on FL-AHNEAP. To reduce the consumption of terminal resources, there is no need to perform multiple rounds of training on the terminal. According to the FL-AHNEAP method, the terminal will train a personalized neural network, so the overlap of the prediction accuracy in the three cases can be explained. **Effect of the number of users on FL-AHNEAP:** Figure 6 compares the effect of the number of users on FL-AHNEAP.