Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation

• URL: http://arxiv.org/abs/2310.11730v4
• Date: Wed, 28 Feb 2024 05:04:34 GMT
• Title: Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
• Authors: Bo Yan, Yang Cao, Haoyu Wang, Wenchuan Yang, Junping Du, Chuan Shi
• Abstract summary: heterogeneous information network (HIN) is a potent tool for mitigating data sparsity in recommender systems. In this paper, we suggest the HIN is partitioned into private HINs stored on the client side and shared HINs on the server. We formalize the privacy definition for HIN-based federated recommendation (FedRec) in the light of differential privacy.
• Score: 45.39171059168941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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