南京邮电大学学报（自然科学版）2025，Vol.45Issue(5)：74-84,11.DOI:10.14132/j.cnki.1673-5439.2025.05.009
基于自适应噪声与混合注意力的联邦学习异常检测
Federated learning anomaly detection based on adaptive noise and hybrid attention
摘要
Abstract
Existing distributed anomaly detection models based on federated learning can hardly deal with the balance between anomaly detection performance and data privacy protection.In this regard,a federated learning anomaly detection model is proposed based on adaptive noise and hybrid attention mechanism.First,built on the convolutional neural network,this model integrates spatial and multi-head hybrid attention mechanisms to extract complex features in a multidimensional and deep manner,en-abling high-precision anomaly detection.Second,based on both local and centralized differential pri-vacy,this model utilizes the adaptive noise and the privacy budget allocation to further improve the pri-vacy and robustness.Validated experiments are exerted on public datasets NSL-KDD and UNSW-NB15.The results show that compared with the existing mainstream approaches,the proposed model can achieve higher-quality anomaly detection while ensuring user data privacy.关键词
联邦学习/异常检测/隐私保护/自适应噪声/混合注意力机制Key words
federated learning/anomaly detection/privacy protection/adaptive noise/hybrid attention mechanism分类
信息技术与安全科学引用本文复制引用
许建,任义,周浩,戴华,杨庚..基于自适应噪声与混合注意力的联邦学习异常检测[J].南京邮电大学学报（自然科学版）,2025,45(5):74-84,11.基金项目
国家自然科学基金(62372244)资助项目 （62372244）
