Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation. (arXiv:2310.11730v1 [cs.LG])

Heterogeneous information network (HIN), which contains rich semantics
depicted by meta-paths, has become a powerful tool to alleviate data sparsity
in recommender systems. Existing HIN-based recommendations hold the data
centralized storage assumption and conduct centralized model training. However,
the real-world data is often stored in a distributed manner for privacy
concerns, resulting in the failure of centralized HIN-based recommendations. In
this paper, we suggest the HIN is partitioned into private HINs stored in the
client side and shared HINs in …

data distributed graph information meta model training network neural network privacy recommendations recommender systems storage systems tool training world