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有效的轻量级隐私数据异常检测解决方案,具有强大的聚合能力

Jiateng Zhao^1,2,3, Bin Wen^4,5,6, Jiashuai Yang^7,8,9

¹Key Laboratory of Data Science and Smart Education, Ministry of Education (Hainan Normal University), Haikou, 571158, China. 202312083900002@hainnu.edu.cn.

Scientific reports

|November 27, 2025

概括

这项研究引入了一个强大的联合学习框架,用于检测私人文本中的异常. 它平衡了隐私,效率和准确性与模型中毒攻击.

科学领域:

人工智能的人工智能
机器学习机器学习
自然语言处理自然语言处理.

背景情况:

联合学习 (FL) 在检测隐私敏感文本中的异常方面存在挑战,原因是模型中毒和效率需求.
现有的方法往往很难在FL设置中平衡稳定性,计算成本和隐私保护.

研究的目的:

在联合学习环境中开发一个完整的框架,用于在隐私敏感文本中进行强大和高效的异常检测.
提高FL对模型中毒攻击的弹性,同时降低推断成本.

主要方法:

开发了一种早期退出的RoBERTa分类器 (E2-RoBERTa),具有多阶段的退出和时空卷积LSTM融合模块.
提出了一个强大的联合分层聚合策略 (RFLA),包括缩小维度,密度聚类和基于Mahalanobis的权重,以实现服务器端的弹性.

主要成果:

E2-RoBERTa实现了高检测准确度,实验显示SMS数据上的[公式:参见文本].
与Krum,Trimmed Mean和Median相比,RFLA在显著的恶意客户百分比下 (20-50%) 显示出更高的准确性和稳定性.
早期退出机制将平均推断时间减少了大约17%.

结论:

综合框架有效地平衡了隐私保护,对模型中毒的稳定性和计算效率.
拟议的E2-RoBERTa和RFLA提供了一个实用的解决方案,用于在联合设置中检测隐私文本异常.
这种方法支持在敏感数据环境中部署安全有效的异常检测系统.

关键词:

联合学习是联合学习.模型中毒防御的模型.私文异常检测检测私文异常检测强大的聚合聚合.

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