哈尔莫FGL:协调GNN隐藏因素为联合图形学习
IEEE transactions on neural networks and learning systems
|January 6, 2026
概括
联合图形学习 (FGL) 通过协调图形神经网络 (GNN) 潜伏因素来解决数据隔离问题. 哈莫FGL框架通过减轻图形异质性和增强聚合机制来改善保护隐私的培训.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 联合图形学习 (FGL) 旨在训练模型在分布式图形数据上,同时保持隐私.
- 现有的FGL方法在图形异质性 (特征和结构偏差) 和有效的聚合策略方面扎.
研究的目的:
- 提出一个新的框架,HarmoFGL,以解决联合图形学习的关键挑战.
- 协调图形神经网络 (GNN) 潜伏因素,以改善跨客户端联合培训.
主要方法:
- 引入了HarmoFGL框架,使用隐性因素视角.
- 开发了一种隐性特征交叉 (IFC) 方法来缓解特征偏差.
- 建立了一个跨客户端的共生参数空间,用于GNN潜伏因素处理结构偏差.
- 实现了基于客户相关性的个性化参数聚合和图形拉普拉斯规范化.
主要成果:
- 哈莫FGL框架有效地协调了GNN隐藏因素,用于联合图形学习.
- 在缓解跨客户的特征和结构偏差方面取得了显著的改进.
- 在各种公共和医疗图形数据集上验证了框架的有效性.
结论:
- 哈莫FGL为保护隐私的联合图形学习提供了一个强大的解决方案.
- 提出的方法有效地解决了图形异质性,并增强了协作培训的好处.
- 该框架在各种现实世界图形数据场景中表现出强的表现.
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