通过对比的自我监督学习来检测联邦图形异常
概括
使用对比自主监督学习 (CSSL) 的联合图形异常检测提高了隐私和准确性. 通过聚合邻近嵌入的数据,FedCAD框架改善了分布式系统中具有有限数据的异常检测.
科学领域:
- 图表机器学习 图表机器学习
- 数据挖掘是一种数据挖掘.
- 网络安全 网络安全
背景情况:
- 属性图形异常检测对于识别复杂图形数据中的异常值至关重要.
- 集中式方法带来隐私风险,而联合式学习提供了一个保护隐私的替代方案.
- 分布式图形数据的局限性阻碍了联合学习用于异常检测的直接应用.
研究的目的:
- 提出使用对比自主监督学习 (CSSL) 的联合图形异常检测框架 (FedCAD).
- 为了解决分布式图形异常检测中的隐私问题和数据限制.
- 为了提高异常检测在联合设置中的效率和精度.
主要方法:
- 联合图形异常检测框架 (FedCAD) 使用对比自主监督学习 (CSSL).
- 伪标签发现用于初步异常节点识别.
- 局部异常邻居嵌入聚合策略以增强区分.
主要成果:
- 通过联合学习 (FL),FedCAD可以有效地更新跨客户端的异常节点信息.
- 聚合策略放大了异常和邻近节点之间的区别.
- 在四个真实图形数据集上的实验结果证明了FedCAD的效率.
结论:
- 对于分布式图形异常检测,FedCAD提供了一个保护隐私和有效的解决方案.
- 通过邻居嵌入聚合增强的对比学习,提高了异常检测效率.
- 拟议的框架克服了在联合环境中有限的客户数据的局限性.
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