Title: Reimagining Federated Knowledge Graph Embedding with One-Shot Adaptive Semantic Alignment
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Knowledge Graph, Federated Learning, One-Shot

Abstract: Knowledge graphs (KGs) are widely used in various knowledge-driven applications, such as the recent surge in large language models. Yet, its adoption across different organizations is often hindered by data silos and privacy concerns, resulting in fragmented knowledge and limited performance. Federated learning (FL) has recently emerged as a potential solution for KG sharing while preserving data privacy. However, most federated KG embedding methods incur high communication and computation costs due to iterative updates and heavy synchronization, yet yield only limited performance gains, with additional privacy leakage risk via multiple communications. To address these challenges, we propose OFKGE, an efficient One-Shot Federated Knowledge Graph Embedding framework. Clients independently train local KGE models and upload them once, eliminating iterative communication. The server distills and aggregates semantic patterns from submitted models to form a generalized global representation via the Adaptive Semantic Alignment (ASA), which is then redistributed as optimized auxiliary knowledge. Clients locally fine-tune their models using this distilled global guide, enhancing inference performance while preserving personalized structural information. OFKGE achieves competitive representation quality with minimal communication, making it suitable for resource-constrained and privacy-sensitive federated settings. Experimental results on the MKG-W, MKG-Y, DB15K, FB15K-237, and NELL-995 datasets demonstrate that OFKGE significantly reduces resource overhead while outperforming state-of-the-art methods on most metrics. The code is available at https://anonymous.4open.science/r/OFKGE-CACB.