Recommendations in Smart Devices Using Federated Tensor Learning

Recommendations based on prediction of user preferences from partial information are widely used in various applications. However, recommendations using smart devices have some challenges related to limited data device resources, data sparsity, and data privacy. Since there are many multidimensional data in smart devices, recommendations may collect a large amount of user private data. In this article, we study privacy-preserving recommendations with high-dimensional tensor data in smart devices. First, we propose a federated tensor completion scheme to infer the user's preferences and we prove that this scheme satisfies the differential privacy guarantee. Our scheme consists of a global update and a local update, which reduce information exposure and guarantee local data privacy. Second, we mathematically analyze the privacy and utility of the proposed algorithm. Third, we provide empirical evaluations on synthetic data sets and real-world data sets. Results show that our scheme has a low recovery error and provides strong privacy protection.