为物联网异常检测设计了带结构稀疏性的联邦主成分分析方法
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
- 通过行稀疏和元素稀疏双重正则化，去除冗余特征并抑制噪声
- 在多个数据集上实现更高检测准确率和更强模型可解释性
- 适合注重隐私保护与模型简洁性的物联网安全场景
尽管联邦学习已成为适用于分布式物联网环境的隐私保护框架，但现有联邦主成分分析方法缺乏稀疏性整合，而稀疏性对鲁棒异常检测至关重要。为此，我们提出一种新型联邦结构稀疏主成分分析（FedSSP）方法，用于物联网网络中的异常检测。该模型创新性地引入双稀疏正则化：(1) 基于ℓ₂,ₚ范数（p∈[0,1)）的行稀疏性以消除冗余特征维度；(2) 基于ℓₚ范数（q∈[0,1)）的元素稀疏性以抑制噪声敏感分量。针对该非凸问题，我们在分布式环境下设计了一种基于近端交替最小化的高效优化算法。数值实验验证，引入结构稀疏性显著提升了模型可解释性与检测精度。代码已公开于https://github.com/xianchaoxiu/FedSSP。
原文摘要 · Abstract (English)
Although federated learning has gained prominence as a privacy-preserving framework tailored for distributed Internet of Things (IoT) environments, current federated principal component analysis (PCA) methods lack integration of sparsity, a critical feature for robust anomaly detection. To address this limitation, we propose a novel federated structured sparse PCA (FedSSP) approach for anomaly detection in IoT networks. The proposed model uniquely integrates double sparsity regularization: (1) row-wise sparsity governed by $\ell_{2,p}$-norm with $p\in [0,1)$ to eliminate redundant feature dimensions, and (2) element-wise sparsity via $\ell_{q}$-norm with $q\in [0,1)$ to suppress noise-sensitive components. To solve this nonconvex problem in a distributed setting, we devise an efficient optimization algorithm based on the proximal alternating minimization (PAM). Numerical experiments validate that incorporating structured sparsity enhances both model interpretability and detection accuracy. Our code is available at https://github.com/xianchaoxiu/FedSSP.
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