  Recommender systems are widely used in industry to improve user experience.
Despite great success, they have recently been criticized for collecting
private user data. Federated Learning (FL) is a new paradigm for learning on
distributed data without direct data sharing. Therefore, Federated Recommender
(FedRec) systems are proposed to mitigate privacy concerns to non-distributed
recommender systems. However, FedRec systems have a performance gap to its
non-distributed counterpart. The main reason is that local clients have an
incomplete user-item interaction graph, thus FedRec systems cannot utilize
indirect user-item interactions well. In this paper, we propose the Federated
Graph Recommender System (FedGRec) to mitigate this gap. Our FedGRec system can
effectively exploit the indirect user-item interactions. More precisely, in our
system, users and the server explicitly store latent embeddings for users and
items, where the latent embeddings summarize different orders of indirect
user-item interactions and are used as a proxy of missing interaction graph
during local training. We perform extensive empirical evaluations to verify the
efficacy of using latent embeddings as a proxy of missing interaction graph;
the experimental results show superior performance of our system compared to
various baselines. A short version of the paper is presented in
\href{https://federated-learning.org/fl-neurips-2022/}{the FL-NeurIPS'22
workshop}.
