To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of training a machine model without needing to know each user’s personal raw private data. In this article, we propose secure matrix factorization framework under the setting, called FedMF. First, design user-level distributed where can be learned when on...