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通过非模型共享方法的联合学习系统对双入账簿记数据进行异常检测
Sota Mashiko1, Yuji Kawamata2, Tomoru Nakayama1
1Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan.
Scientific reports
|November 26, 2025
概括
本研究引入了一种新的数据协作 (DC) 框架,用于在财务审计中检测异常. 它可以在组织中进行安全的分析,而无需共享原始数据或需要持续的网络连接,从而增强审计智能.
科学领域:
- 财务审计 财务审计 财务审计
- 机器学习 机器学习
- 数据安全 数据安全
背景情况:
- 在财务审计中检测异常需要来自多个组织的大量数据.
- 由于对机密性的担忧,会计师事务所之间无法进行传统的数据共享.
- 现有的联合学习 (FL) 方法涉及广泛的沟通和网络接触.
研究的目的:
- 开发非模型共享的FL框架,用于在财务审计中检测异常.
- 为了实现安全的数据协作,而不暴露原始数据或需要持续的网络连接.
- 提高审计中异常检测的效率和保密性.
主要方法:
- 提出了一个数据协作 (DC) 分析框架,一种非模型共享的FL技术.
- 利用缩小维度来实现安全的中间数据表示.
- 采用基于协作表示的自动编码器,只需要一个通信回合.
主要成果:
- 基于直流的方法比本地训练的模型和传统的FL方法 (FedAvg,FedProx) 更好.
- 该框架表现出卓越的性能,特别是在non-i.i.d.下. 条件,条件,条件,条件.
- 在保持数据保密的同时,实现了有效的异常检测.
结论:
- 组织知识可以集成到高级审计中,同时保持数据保密性.
- DC框架为智能审计系统提供了一个实用的解决方案.
- 这种方法有助于在合作财务审计中安全有效地检测异常.
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