###### Abstract  
At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users' privacy. mobile app start-up prediction; federated learning; app usage; prediction +
Footnote †: journal:  
the proportion of the time used by the installed software in the software, which is used to predict which application will be the next to operate. The algorithms cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. Although there are mobile App start-up prediction algorithms based on multi-user data, the integration of multi-party data is required, which can easily lead to the leakage of user privacy data [2]. The current general solution is the distributed learning of centralized computing, which collects user data, uploads it to the central server, and conducts distributed learning in the central server to obtain the recommended model, and then recommends items that users may be interested in based on the model and the current situation. In the whole process, the user serves only as the producer of the data. After the data is gathered on the central server, the user will lose the ownership of the data and cannot control the storage, access, usage, or flow of the data. Consequently, the risk of information being leaked, misused, or even used for improper profit is increasing. As for big data systems, they have organizational boundaries and lack of matched solutions for supervision and compliance operations. However, in the era of big data where privacy protection has attracted much attention, various countries have begun to attach importance to the legislation in terms of privacy protection. Data producers still lack the control over data, and due to the frequent occurrence of personal information leakage, users have more distrust of data custodians. The Federated Learning [3] solution proposed by Google AI Lab in 2016 can alleviate the problem of data privacy leakage in centralized data recommendation. The basic idea of federated learning is to store the data locally, train the model locally, and then upload the model parameters to the cloud to perform the model aggregation. To address the above problems, we propose the FL-AHNEAP method based on the federated learning and AHNEAP [4] method. The FL-AHNEAP method can realize the mobile App start-up prediction under the condition of privacy protection, so as to adapt to the mobile App start-up prediction scenario. While integrating multi-user data, the idea of federated learning is applied to alleviate the privacy leakage problem in the distributed learning of centralized computing. At the same time, it can alleviate the cold start problem of users to a certain extent. The paper comprises the following parts: Section 1. Review of current research results of mobile App start-up prediction and federated learning. Section 2. A multi-user data prediction method, FL-AHNEAP, is proposed based on federated learning and the AHNEAP method. Section 3. The terminal load and communication overhead of the FL-AHNEAP method are analyzed based on the Android terminal. Section 4. We use the LiveLab dataset to conduct related experiments on the proposed method and analyze the influence of various factors on FL-AHNEAP. Section 5.