  Federated Learning (FL) has emerged as a promising approach for preserving
data privacy in recommendation systems by training models locally. Recently,
Graph Neural Networks (GNN) have gained popularity in recommendation tasks due
to their ability to capture high-order interactions between users and items.
However, privacy concerns prevent the global sharing of the entire user-item
graph. To address this limitation, some methods create pseudo-interacted items
or users in the graph to compensate for missing information for each client.
Unfortunately, these methods introduce random noise and raise privacy concerns.
In this paper, we propose FedRKG, a novel federated recommendation system,
where a global knowledge graph (KG) is constructed and maintained on the server
using publicly available item information, enabling higher-order user-item
interactions. On the client side, a relation-aware GNN model leverages diverse
KG relationships. To protect local interaction items and obscure gradients, we
employ pseudo-labeling and Local Differential Privacy (LDP). Extensive
experiments conducted on three real-world datasets demonstrate the competitive
performance of our approach compared to centralized algorithms while ensuring
privacy preservation. Moreover, FedRKG achieves an average accuracy improvement
of 4% compared to existing federated learning baselines.
