Federated Structured Sparse PCA for Anomaly Detection in IoT Networks

• URL: http://arxiv.org/abs/2503.23981v1
• Date: Mon, 31 Mar 2025 11:50:21 GMT
• Title: Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
• Authors: Chenyi Huang, Xinrong Li, Xianchao Xiu,
• Abstract summary: We propose a novel federated anomaly minimization approach in IoT networks.<n>The proposed model integrates row-wise sparsity governed by $ell_2, sparse$.<n>Experiments prove that incorporating structured sparsity enhances both model interpretability.
• Score: 1.4500146354034478
• 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 efficiently solve this non-convex optimization problem in a distributed setting, we devise a proximal alternating minimization (PAM) algorithm with rigorous theoretical proofs establishing its convergence guarantees. Experiments on real datasets validate that incorporating structured sparsity enhances both model interpretability and detection accuracy.

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