Temporal graph clustering is a complex task that involves discovering meaningful structures in dynamic graphs where relationships and entities change over time. Existing methods typically require centralized data collection, which poses significant privacy and communication challenges. In this work, we introduce a novel Federated Temporal Graph Clustering (FTGC) framework that enables decentralized training of graph neural networks (GNNs) across multiple clients, ensuring data privacy throughout the process. Our approach incorporates a temporal aggregation mechanism to effectively capture the evolution of graph structures over time and a federated optimization strategy to collaboratively learn high-quality clustering representations. By preserving data privacy and reducing communication overhead, our framework achieves competitive performance on temporal graph datasets, making it a promising solution for privacy-sensitive, real-world applications involving dynamic data.

本研究解决了时序图聚类中数据集中化导致的隐私和通信挑战。提出的联邦时序图聚类（FTGC）框架通过去中心化的训练方式、时序聚合机制和联邦优化策略，有效捕捉图结构的演变并确保数据隐私。研究表明，该方法在时序图数据集上表现出色，适合隐私敏感的动态数据应用。