  Recommender systems have shown to be a successful representative of how data
availability can ease our everyday digital life. However, data privacy is one
of the most prominent concerns in the digital era. After several data breaches
and privacy scandals, the users are now worried about sharing their data. In
the last decade, Federated Learning has emerged as a new privacy-preserving
distributed machine learning paradigm. It works by processing data on the user
device without collecting data in a central repository. We present FedeRank
(https://split.to/federank), a federated recommendation algorithm. The system
learns a personal factorization model onto every device. The training of the
model is a synchronous process between the central server and the federated
clients. FedeRank takes care of computing recommendations in a distributed
fashion and allows users to control the portion of data they want to share. By
comparing with state-of-the-art algorithms, extensive experiments show the
effectiveness of FedeRank in terms of recommendation accuracy, even with a
small portion of shared user data. Further analysis of the recommendation
lists' diversity and novelty guarantees the suitability of the algorithm in
real production environments.
