FedHGPrompt: 保护隐私的联合快速学习,用于少数拍摄的异质图形学习
Xijun Wu1, Jianjun Shi1, Xinming Zhang1
1School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了FedHGPrompt,这是一个用于异质图的联合学习框架. 它增强了短暂的学习性能,同时通过安全聚合确保了数据隐私.
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:
- 从异质图表学习面临着数据稀缺性和隐私方面的挑战.
- 联合学习和图表提示学习提供解决方案,但对于复杂的图表来说很难整合.
研究的目的:
- 提出FedHGPrompt,一个用于异质图形学习的新型联合框架.
- 在分散式图形学习中解决数据稀缺性和隐私问题.
主要方法:
- 一个三层架构:统一,适应和隐私层.
- 双模板用于图形/任务标准化和可训练的双提示,用于几次拍摄的适应.
- 为强大的数据隐私提供加密安全聚合协议.
主要成果:
- 在现实数据集 (ACM,DBLP,Freebase) 上,FedHGPrompt的性能优于现有的联合图表学习基线.
- 在强大的隐私保证下,实现了优越的几次射击学习性能.
- 证明了实际的沟通效率.
结论:
- FedHGPrompt提供了一种有效的方法,用于在分布式,隐私敏感的异质图上进行协作学习.
- 该框架成功地集成了对复杂的图形结构的少量学习和联合学习.
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