  Recommender systems have become ubiquitous in the past years. They solve the
tyranny of choice problem faced by many users, and are utilized by many online
businesses to drive engagement and sales. Besides other criticisms, like
creating filter bubbles within social networks, recommender systems are often
reproved for collecting considerable amounts of personal data. However, to
personalize recommendations, personal information is fundamentally required. A
recent distributed learning scheme called federated learning has made it
possible to learn from personal user data without its central collection.
Consequently, we present a recommender system for movie recommendations, which
provides privacy and thus trustworthiness on multiple levels: First and
foremost, it is trained using federated learning and thus, by its very nature,
privacy-preserving, while still enabling users to benefit from global insights.
Furthermore, a novel federated learning scheme, called FedQ, is employed, which
not only addresses the problem of non-i.i.d.-ness and small local datasets, but
also prevents input data reconstruction attacks by aggregating client updates
early. Finally, to reduce the communication overhead, compression is applied,
which significantly compresses the exchanged neural network parametrizations to
a fraction of their original size. We conjecture that this may also improve
data privacy through its lossy quantization stage.
