保护隐私的动态图聚类框架，支持多方协作训练
Federated Temporal Graph Clustering
- 基于联邦学习实现跨客户端的动态图聚类
- 通过时间聚合捕捉图结构演化，性能优于传统方法
- 适合隐私敏感场景，如医疗、金融动态数据建模
动态图聚类需发现随时间变化的关系与实体结构。现有方法依赖集中式数据收集，带来隐私与通信挑战。本文提出联邦时序图聚类（FTGC）框架，支持多客户端分布式训练图神经网络，全程保障数据隐私。方法引入时间聚合机制以捕捉图结构演化，并采用联邦优化策略协同学习高质量聚类表示。该框架在保持隐私的同时降低通信开销，在多个时序图数据集上取得竞争性性能，为涉及动态数据的隐私敏感应用场景提供可行方案。
原文摘要 · Abstract (English)
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.
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