Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation

arXiv:2310.11730v3 Announce Type: replace-cross
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 …

arxiv cs.ai cs.cr cs.dc cs.lg data distributed federated graph information meta model training network neural network privacy real recommender systems storage systems tool training under world