Enhancing Privacy-Preserving Knowledge Graph Embeddings with Federated Learning for IoT Services

As a structured representation of real-world facts, knowledge graphs (KGs) play a vital role in IoT applications, due to their strong reasoning capabilities and interpretability. However, private user IoT KG data often needs to be centrally collected for embedding training, which poses significant privacy risks and limits the scalability of knowledge-driven downstream applications in distributed IoT environments. Federated learning (FL) has emerged as a promising solution for decentralized model training, eliminating the need for direct data collection. However, existing federated knowledge graph embedding (KGE) methods often struggle to preserve the inherent graph structure of entities and relations, leading to fragmented and incomplete representations. Additionally, they struggle to effectively capture diverse relational dependencies within personal KGs. To address these challenges, this article proposes an enhanced federated KG embedding method for personal knowledge sharing (FPKS) to enable privacy-preserving KGE training. The FPKS framework consists of a central server and multiple federated clients. To enhance entity and relationship alignment across clients, FPKS maintains separate embedding tables for entities and relationships on the server. Moreover, to capture the structural and contextual information of personal KGs, we introduce a local encoder-decoder architecture that employs a graph convolutional network (GCN) variant as an encoder and a KGE scoring function as a decoder. Furthermore, we propose a bidirectional composite operator for GCN (BiDGCN) to enhance multi-relational information aggregation. Extensive experiments on two widely used KG datasets demonstrate that FPKS significantly outperforms existing methods, improving the quality of learned embeddings while ensuring data privacy. Our approach facilitates decentralized personal knowledge sharing, marking an advancement in secure and efficient IoT knowledge-driven services.