Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation. (arXiv:2203.09553v1 [cs.AI])

Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well
studied as other domains, such as computer vision and natural language
processing. A recent study FedE first proposes an FL framework that shares
entity embeddings of KGs across all clients. However, compared with model
sharing in vanilla FL, entity embedding sharing from FedE would incur severe
privacy leakage. Specifically, the known entity embedding can be used to infer
whether a specific relation between two entities exists in a private client. In
this paper, we first develop a novel attack that aims to recover the original
data based on embedding information, which is further used to evaluate the
vulnerabilities of FedE. Furthermore, we propose a Federated learning paradigm
with privacy-preserving Relation embedding aggregation (FedR) to tackle the
privacy issue in FedE. Compared to entity embedding sharing, relation embedding
sharing policy can significantly reduce the communication cost due to its
smaller size of queries. We conduct extensive experiments to evaluate FedR with
five different embedding learning models and three benchmark KG datasets.
Compared to FedE, FedR achieves similar utility and significant (nearly 2X)
improvements in both privacy and efficiency on link prediction task.

Source: https://arxiv.org/abs/2203.09553

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