FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning

作者：Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang, Jia Liu, Tianrui Li · 发表于：International Journal of Computational Intelligence Systems · 年份：2025 · DOI：10.1007/s44196-025-00878-5 · 被引用次数：4 · 研究领域：Advanced Graph Neural Networks、Privacy-Preserving Technologies in Data、Recommender Systems and Techniques

As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information. But they have not yet combined temporal information at different granularities. Also, in the case of multiple knowledge graphs distributed across different clients, it is of interest to ensure that the knowledge graph embedding representations are learned without exposing data and collaboratively. Therefore, in this paper, we propose a framework for multi-granularity dynamic knowledge graph embedding in federated learning (FedMDKGE), which allows multiple parties to interact securely with temporal information at different granularities. In the client, we present a multi-granularity dynamic knowledge graph embedding model that improves the capability of dynamic knowledge graph embedding representation by focusing on multi-granularity temporal facts from the perspective of knowledge utilization. On the server, we design a multi-granularity aggregation rule to accommodate multi-party information aggregation at different granularities. Finally, we conduct extensive experiments to demonstrate the superior performance of our model. The results on these real datasets show that FedMDKGE considering multi-granularity temporal information performs better than all comparative baselines and interconnect information for multi-party dynamic knowledge graph embedding without exposing data.