Federated Structured Sparse PCA for Anomaly Detection in IoT Networks

• URL: http://arxiv.org/abs/2503.23981v3
• Date: Tue, 28 Oct 2025 04:55:22 GMT
• Title: Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
• Abstract summary: We propose a novel federated principal component analysis (PCA) approach for anomaly in IoT networks.<n>The proposed model integrates sparsity regularization governed by $ell_2, $Pnormin [0,1)<n>We devise an efficient optimization algorithm based on the alternating structured proximal sparsity setting.
• Score: 3.7711905543608695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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