KGFedRS: Knowledge Graph enhanced Federated Recommender System

Federated recommender systems are designed to learn user preferences without violating user privacy where sensitive user-item interactions are kept in local client and models are trained collaboratively without centrally aggregating raw data. However, the on-device local training strategy results in serious data sparsity problem which reduces the accuracy of recommendation. In this paper, we propose KGFedRS, a novel knowledge graph(KG) enhanced Federated Recommender System which not only protects the privacy of both users and knowledge graph, but also alleviates the data sparsity problem by incorporating auxiliary information from KGs. First, we propose a privacy-preserving framework for KG-based federated recommendation by introducing a third-party server to orchestrate the encrypted data matching between KG and user profile without privacy leakage. Second, a novel KG-guided implicit interaction subgraph generation module is presented aiming at learning the implicit collaborative signals for each client. Meanwhile, a local subgraph expansion module is introduced to capture the explicit high-order collaborative information. Extensive comparative experiments on three public datasets demonstrate that the proposed KGFedRS outperforms the state-of-the-art federated recommendation methods in terms of effectiveness and efficiency. The datasets and source code are available at https://github.com/IHTWDhhh/KG4FedRS.