  The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance.
