# Federated Structured Sparse PCA for Anomaly Detection in IoT Networks

arXiv: https://arxiv.org/abs/2503.23981 · HTML: https://mlsanity.com/paper/2503.23981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (2024)
- [Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection](https://mlsanity.com/paper/2012.14595/) (2020)
- [Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion](https://mlsanity.com/paper/2110.08774/) (2021)
- [Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey](https://mlsanity.com/paper/2310.10549/) (2023)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Principal Component Analysis: A Natural Approach to Data Exploration](https://mlsanity.com/paper/1804.02502/) (2018)
- [Bi-Sparse Unsupervised Feature Selection](https://mlsanity.com/paper/2412.16819/) (2024)
- [Scalable and Privacy-Preserving Federated Principal Component Analysis](https://mlsanity.com/paper/2304.00129/) (2023)
- [Revisiting Lq(0≤ q<1) Norm Regularized Optimization](https://mlsanity.com/paper/2306.14394/) (2023)
- [Federated PCA on Grassmann Manifold for IoT Anomaly Detection](https://mlsanity.com/paper/2407.07421/) (2024)

## Abstract (arXiv)

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 ℓ2,p-norm with p∈ [0,1) to eliminate redundant feature dimensions, and (2) element-wise sparsity via ℓq-norm with q∈ [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.