## 3 FL-Ahneap for Mobile App Start-Up Prediction under Privacy Protection  
### Basic Idea  
The AHNEAP [4] method performs mobile App start-up prediction based on a single-user data training model. It is a mobile App startup prediction method based on representation learning on the attributed heterogeneous network. In an attributed heterogeneous network, there are different nodes, and each node has its own attribute information. There are also multiple edge relationships between the nodes. In this article, this means that a network contains three kinds of nodes: time, location, and application. Each node has its own attribute information. Accordingly, there are three types of edge relationships between time and application, location and application, and preorder application. The method includes three steps:  
* Data pre-processing: extracting time, location, App information, and their relationships from the user's historical App usage records to generate a heterogeneous network, and assigning attribute information to each node in the network;
* Representation learning on the attributed heterogeneous network: employing the random walk method in the attributed heterogeneous network to generate training sample pairs to train the representation learning model for the attributed heterogeneous network;
* Link prediction model based on the neural network: integrating three pieces of contextual information--time, location, and previous App--to predict the probability of links jointly generated by current time, location, previous App node, and other App nodes. Moreover, the processing of new nodes is included in the design of the AHNEAP method, and the new Apps in the network are represented by the new nodes. Therefore, the AHNEAP method alleviates the cold start problem of new Apps to a certain extent. In practical situations, users with few or no historical records cannot be predicted according to the AHNEAP method, and the integration of multi-user data can mine the correlation between users and alleviate the cold start problem of new users to a certain extent. At present, the training based on multi-user data generally depends on centralized computing of data, and historical records generated by each user need to be stored in a centralized manner. The AHNEAP method is a context-based prediction method, and the leakage of context information may result in risks to the safety of the user's property or life. Therefore, users' privacy issues need to be considered in the context of improvement based on the AHNEAP method to integrate multi-user data. The idea of federated learning is proposed to alleviate the risk of privacy leakage in data centralized computing. In the mobile App start-up prediction scenario, the training process of each user is the same. Therefore, we intend to improve the AHNEAP method by integrating the multi-user App usage records, which is the basic idea of horizontal federated learning. Based on the idea of federated learning, the data pre-processing step is performed locally, and in order to ensure the user's personalized prediction, the FL-AHNEAP method only applies federated learning to the representation learning on the attributed heterogeneous network, and the link prediction step based on the neural network is still performed locally. To summarize, FL-AHNEAP contains the following three steps:  
* Data pre-processing: In the context of federated learning, model training is performed on the terminal, and obviously data pre-processing is also performed on the terminal;
* Network representation learning under federated learning: Based on the idea of federated learning, the representation learning model for the attributed heterogeneous network is used to integrate multi-user data, and the FederatedAveraging algorithm is used as the optimization algorithm;
* Personalized link prediction: The personalized link prediction model is trained based on the neural network.