Abstract
Federated recommender system (RS), a prevailing distributed paradigm, has been spawning significant interest in exploiting locally stored but tremendous data to predict items best aligned with clients. However, federated RS suffers severely from a single point of failure due to the dependency on the central server, leading to potential denial of service (DoS) attacks. To address this security weakness, in this paper, we propose a decentralized privacy-preserving federated graph convolutional network for RS, dubbed DeFedGCN. Specifically, DeFedGCN aggregates local updates by a decentralized consensus-reaching process and customizes local models for personalized recommendation, where the aggregation is enhanced by local differential privacy to resist model inversion attacks. More importantly, to promote the recommendation performance, DeFedGCN conducts a sub-graph expansion based on the private set interaction to explore high-order interactions among clients and items. Theoretical analysis confirms the effectiveness and privacy guarantee of DeFedGCN. Additionally, we conduct extensive experiments on four widespread real-world databases. The recommendation performance of DeFedGCN outperforms the state-of-the-art federated RS algorithms without security protection against DoS attacks by up to 7.4%.