FedPPD:通过伪原型蒸实现有效的子图联合学习.
Qi Lin1, Jishuo Jia1, Yinlin Zhu2
1Shandong University, School of Mechanical, Electrical and Information Engineering, Weihai, 264209, China.
概括
亚图联合学习 (亚图FL) 面临亚图异质性,阻碍了全球模型的性能. 我们的FedPPD方法使用伪原型蒸来有效地传输知识,优于现有的子图FL方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 亚图联网学习 (subgraph-FL) 允许跨客户端的图形神经网络 (GNN) 的协作训练.
- 在现实世界中,子图FL受到子图异质性的挑战,导致全球模型的性能下降.
- 现有的方法通常依赖于参数聚合,过度简化子图知识,导致次优推理.
研究的目的:
- 提出一种新的方法,FedPPD (联合伪原型蒸),以解决FL-FL.亚图中的亚图异质性.
- 提高全球GNN模型在分散式图形学习场景中的性能.
- 有效地将丰富的子图知识转移到全球模型中,而不过于简化.
主要方法:
- FedPPD使用由本地原型引导的发电机来探索全球输入空间.
- 伪图表是为了将当地GNN的知识提炼到汇总的全球GNN而生成的.
- 这个过程旨在传达在标准聚合过程中丢失的细微子图信息.
主要成果:
- 在子图FL任务中,FedPPD的表现始终超过了最先进的基线.
- 六个公共数据集的实验验证证证了拟议方法的有效性.
- 伪原型蒸方法增强了全球模型利用多样化的子图数据的能力.
结论:
- 在联邦学习中,FedPPD有效地减轻了子图异质性的负面影响.
- 建议的知识蒸策略为全球GNN推断提供了比简单的参数聚合更优质的替代方案.
- 在实现强大和高性能子图FL系统方面,FedPPD代表了重大进步.
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