Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
This work addresses robust anomaly detection in IoT networks by enhancing federated learning with structured sparsity, though it is incremental as it builds on existing federated PCA methods.
The paper tackled the lack of sparsity in federated PCA for IoT anomaly detection by proposing a federated structured sparse PCA (FedSSP) approach, which improved detection accuracy and model interpretability through double sparsity regularization.
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.