## 4 Terminal Load and Communication
### Analysis of Terminal Load
We use following three kinds of training data for testing  
* One month's data of user A11: a total of 745 records; 170 nodes and 2550 training sample pairs were generated;
* One month's data of user D03: a total of 2718 records; 570 nodes and 6694 training sample pairs were generated;
* Data of user B02 in the past year: 14,565 records in total; 3427 nodes and 37,602 training sample pairs were generated. The test results are shown in Table 1. The values in the table are the mean values after multiple tests. The total running time includes the whole step of data pre-processing, training of the representation learning model for the attributed heterogeneous network, training of the link prediction model based on the neural network, and model testing, whereas the data processing time only includes the time consumed by the data pre-processing step. Obviously, the mobile phone configuration will affect the time consumption. The configuration of P20 is better than that of Nova2S. Therefore, P20 generally consumes less time than Nova2S in both data processing time and total running time. The total running time of P20 is about 56\(\sim\)68% of that of Nova2S, whereas the data processing time is about 60\(\sim\)70%. The total running time minus the data processing time yields the time for model training and model testing; this time for P20 is about 51\(\sim\)65% of that of Nova2S. Through the analysis of the test results, we can learn that the more training data, the longer the consumption time; the less training data, the shorter the consumption time; and the total running time increases exponentially similar to the number of training sample  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **User** & \begin{tabular}{c} **Number of** \\ **Records** \\ \end{tabular}  & \begin{tabular}{c} **Number of** \\ **Nodes** \\ \end{tabular}  & \begin{tabular}{c} **Training** \\ **Sample Pairs** \\ \end{tabular}  & \ \begin{tabular}{c} **Data Processing** \\ **Time** \\ \end{tabular}  & \begin{tabular}{c} **Total Running** \\ **Time** \\ \end{tabular}  \\ \hline P20 & A11 & 745 & 170 & 2550 & 29\({}^{\prime\prime}\) & 1\({}^{\prime}\)20\({}^{\prime\prime}\) \\  & D03 & 2718 & 570 & 6694 & 48\({}^{\prime\prime}\) & 3\({}^{\prime}\)7\({}^{\prime\prime}\) \\  & B02 & 14,565 & 3427  
pairs. As the user with the most historical records in a month, user D03 spends 37\({}^{\prime\prime}\) on P20, and 4\({}^{\prime}\)35\({}^{\prime\prime}\) on Nova2S. As one of the users with the most historical records in a year, user B02 spends 32\({}^{\prime}\)43\({}^{\prime\prime}\) on P20 and 56\({}^{\prime}\)37\({}^{\prime\prime}\) on Nova2S. It can be seen that when the amount of training data is within a certain range, the consumption of the terminal load in the FL-AHNEAP method is still within an acceptable range. However, as the training data grows, the running time also increases, and it takes nearly half an hour or even an hour of training, which is unfriendly for users. Therefore, we still need to consider a variant of the FL-AHNEAP method in the future to make it an incremental training method. At present, the RAM of most users' smart phones is still 4 GB, but the RAM of newly launched mobile phones is generally 6 GB, and mobile phones with 8 GB RAM are continuously being introduced. Apparently, from the perspective of space complexity, the space consumption of RAM in the terminal is sufficient for FL-AHNEAP. In addition, the storage capacity of smart phones is constantly expanding, from 64 G to 128 G, and gradually to 256 G; this capacity is enough to store the historical records continuously generated by users. Similarly, however, as a result of the growth of time, the storage capacity occupied by historical records is also increasing. Although the capacity is sufficient, occupying too much capacity of the mobile phone is not friendly to the user experience. Therefore, it is necessary to consider a variant of the FL-AHNEAP method in the future to make it an incremental training method.