  The heterogeneous information network (HIN), which contains rich semantics
depicted by meta-paths, has emerged as a potent tool for mitigating data
sparsity in recommender systems. Existing HIN-based recommender systems operate
under the assumption of centralized storage and model training. However,
real-world data is often distributed due to privacy concerns, leading to the
semantic broken issue within HINs and consequent failures in centralized
HIN-based recommendations. In this paper, we suggest the HIN is partitioned
into private HINs stored on the client side and shared HINs on the server.
Following this setting, we propose a federated heterogeneous graph neural
network (FedHGNN) based framework, which facilitates collaborative training of
a recommendation model using distributed HINs while protecting user privacy.
Specifically, we first formalize the privacy definition for HIN-based federated
recommendation (FedRec) in the light of differential privacy, with the goal of
protecting user-item interactions within private HIN as well as users'
high-order patterns from shared HINs. To recover the broken meta-path based
semantics and ensure proposed privacy measures, we elaborately design a
semantic-preserving user interactions publishing method, which locally perturbs
user's high-order patterns and related user-item interactions for publishing.
Subsequently, we introduce an HGNN model for recommendation, which conducts
node- and semantic-level aggregations to capture recovered semantics. Extensive
experiments on four datasets demonstrate that our model outperforms existing
methods by a substantial margin (up to 34% in HR@10 and 42% in NDCG@10) under a
reasonable privacy budget.
