Privacy-preserving Decentralized Learning of Knowledge Graph Embeddings

Knowledge Graphs (KGs) enhance the performance of machine learning applications, such as recommendation systems and drug discovery. This is achieved through vector representations of KGs semantics, called Knowledge Graph Embeddings (KGEs). However, obtaining adequate data to train high-quality KGEs can be challenging for individual service providers. FedE and FedR address this challenge by enabling federated learning of KGEs without sharing local KGs, but they are limited by their reliance on trusted servers and lack of protection against inference attacks. Recently, FKGE has been proposed to enable collaboration between providers in the training of KGEs, exploiting differential privacy. Nevertheless, updating KGEs from all providers is time-consuming, and it does not protect against poisoning and backdoor attacks. Following this research direction, this paper focuses on the security and privacy requirements for decentralized learning of KGEs, presents a reference architecture to support these requirements, and discusses its security and privacy limitations.