  In the mobile Internet era, the recommender system has become an
irreplaceable tool to help users discover useful items, and thus alleviating
the information overload problem. Recent deep neural network (DNN)-based
recommender system research have made significant progress in improving
prediction accuracy, which is largely attributed to the access to a large
amount of users' personal data collected from users' devices and then centrally
stored in the cloud server. However, as there are rising concerns around the
globe on user privacy leakage in the online platform, the public is becoming
anxious by such abuse of user privacy. Therefore, it is urgent and beneficial
to develop a recommender system that can achieve both high prediction accuracy
and high degree of user privacy protection.
  To this end, we propose a DNN-based recommendation model called PrivRec
running on the decentralized federated learning (FL) environment, which ensures
that a user's data never leaves his/her during the course of model training. On
the other hand, to better embrace the data heterogeneity commonly existing in
FL, we innovatively introduce a first-order meta-learning method that enables
fast in-device personalization with only few data points. Furthermore, to
defense from potential malicious participant that poses serious security threat
to other users, we develop a user-level differentially private DP-PrivRec model
so that it is unable to determine whether a particular user is present or not
solely based on the trained model. Finally, we conduct extensive experiments on
two large-scale datasets in a simulated FL environment, and the results
validate the superiority of our proposed PrivRec and DP-PrivRec.
