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.

翻译：异质信息网络（HIN）通过元路径刻画丰富语义，已成为缓解推荐系统中数据稀疏问题的有效工具。现有基于HIN的推荐系统假设数据集中存储与模型集中训练，然而实际场景中数据常因隐私需求呈分布式分布，导致HIN内语义断裂问题，使得集中式HIN推荐方法失效。本文提出将HIN划分为客户端私有HIN与服务器共享HIN，并基于此设置设计联邦异构图表征网络（FedHGNN）框架。该框架利用分布式HIN实现推荐模型的协同训练，同时保护用户隐私。具体而言，我们首先依据差分隐私理论形式化定义了基于HIN的联邦推荐（FedRec）的隐私规范，目标在于保护私有HIN中的用户-物品交互关系及共享HIN中的用户高阶模式。为恢复断裂的元路径语义并确保隐私保护措施的有效性，我们精心设计了一种语义保持的用户交互发布方法，通过对用户高阶模式及相关交互信息进行本地扰动后发布。随后引入面向推荐的HGNN模型，通过节点级与语义级聚合机制捕获恢复后的语义信息。在四个数据集上的广泛实验表明，在合理隐私预算下，本模型在HR@10与NDCG@10指标上分别较现有方法提升高达34%和42%。