FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
This work addresses privacy concerns in federated recommendation systems for users and service providers, offering an incremental improvement over existing methods.
The paper tackles the problem of preserving data privacy in federated recommendation systems by proposing FedRKG, which uses a global knowledge graph and relation-aware GNN to enhance user-item interactions while employing pseudo-labeling and Local Differential Privacy for protection, resulting in competitive performance with centralized algorithms and a 4% average accuracy improvement over federated baselines.
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.