Title: Secure Federated Learning of User Verification Models. Abstract: We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.  To address this problem, we propose Federated User Verification (FedUV), a framework for private and secure training of UV models. In FedUV, users jointly learn a set of vectors and maximize the  correlation  of  their  instance  embeddings  with  a  secret  user-defined  linear combination of those vectors. We show that choosing the linear combinations from the codewords of an error-correcting code allows users to collaboratively train the model without revealing their embedding vectors.  We present the experimental results for user verification with voice, face, and handwriting data and show that FedUV is on par with existing approaches, while not sharing the embeddings with other users or the server.