  Recommender systems have become prosperous nowadays, designed to predict
users' potential interests in items by learning embeddings. Recent developments
of the Graph Neural Networks~(GNNs) also provide recommender systems with
powerful backbones to learn embeddings from a user-item graph. However, only
leveraging the user-item interactions suffers from the cold-start issue due to
the difficulty in data collection. Hence, current endeavors propose fusing
social information with user-item interactions to alleviate it, which is the
social recommendation problem. Existing work employs GNNs to aggregate both
social links and user-item interactions simultaneously. However, they all
require centralized storage of the social links and item interactions of users,
which leads to privacy concerns. Additionally, according to strict privacy
protection under General Data Protection Regulation, centralized data storage
may not be feasible in the future, urging a decentralized framework of social
recommendation. To this end, we devise a novel framework \textbf{Fe}drated
\textbf{So}cial recommendation with \textbf{G}raph neural network (FeSoG).
Firstly, FeSoG adopts relational attention and aggregation to handle
heterogeneity. Secondly, FeSoG infers user embeddings using local data to
retain personalization. Last but not least, the proposed model employs
pseudo-labeling techniques with item sampling to protect the privacy and
enhance training. Extensive experiments on three real-world datasets justify
the effectiveness of FeSoG in completing social recommendation and privacy
protection. We are the first work proposing a federated learning framework for
social recommendation to the best of our knowledge.
