Federating recommendations using differentially private prototypes

Machine learning methods exploit similarities in users’ activity patterns to provide recommendations in applications across a wide range of fields including entertainment, dating, and commerce. However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn recommendation models without accessing the sensitive data and without inadvertently leaking private information? Many situations in the medical field prohibit centralizing the data from different hospitals and thus require learning from information kept in separate databases. We propose a new federated approach to learning global and local private models for recommendation without collecting raw data, user statistics, or information about personal preferences. Our method produces a set of locally learned prototypes that allow us to infer global behavioral patterns while providing differential privacy guarantees for users in any database of the system. By requiring only two rounds of communication, we both reduce the communication costs and avoid excessive privacy loss associated with typical federated learning iterative procedures. We test our framework on synthetic data, real federated medical data, and a federated version of Movielens ratings. We show that local adaptation of the global model allows the proposed method to outperform centralized matrix-factorization-based recommender system models, both in terms of the accuracy of matrix reconstruction and in terms of the relevance of recommendations, while maintaining provable privacy guarantees. We also show that our method is more robust and has smaller variance than individual models learned by independent entities.