A Hybrid Decentralised Learning Topology for Recommendations with Improved Privacy.

Many recent studies have investigated the extent to which decentralised topologies for machine learning can preserve privacy, showing that in various scenarios the exchanged model updates can leak user information. In this work, we analyse the privacy level of various decentralised topologies for Federated Learning as applied to Recommender Systems (RS), and propose an alternative hybrid topology as a first step to improve privacy, without considering solutions such as encryption or differential privacy, which can be used on top of the proposed topology. We show that an Anonymous Random Walks (ARW) topology can be used to alleviate privacy concerns in federated RS. We measure the information leakage for each topology as a metric for privacy. Further, we design privacy attacks specific to distributed RS and explore the effect of these attacks on the different topologies with respect to user privacy. Through experiments on three public datasets, we show that the choice of topology involves a significant trade off between communication efficiency and privacy.