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[2602.12622] Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection
Summary
This article presents a novel method for anomaly detection in IoT networks using Efficient Personalized Federated PCA, addressing the challenges of personalization and robustness in federated learning.
Why It Matters
As IoT networks become increasingly prevalent, ensuring their security against anomalies is critical. This research offers a solution that enhances privacy and performance in distributed environments, making it relevant for both academic research and practical applications in cybersecurity.
Key Takeaways
- Introduces Efficient Personalized Federated PCA for IoT anomaly detection.
- Enhances personalization and robustness in federated learning frameworks.
- Utilizes manifold optimization and ADMM for effective problem-solving.
- Demonstrates superior performance over existing methods with higher F1-scores.
- Code availability supports further research and application development.
Computer Science > Machine Learning arXiv:2602.12622 (cs) [Submitted on 13 Feb 2026] Title:Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection Authors:Xianchao Xiu, Chenyi Huang, Wei Zhang, Wanquan Liu View a PDF of the paper titled Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection, by Xianchao Xiu and 3 other authors View PDF HTML (experimental) Abstract:Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy-preserving framework for distributed IoT environments, current federated principal component analysis (PCA) methods lack the integration of personalization and robustness, which are critical for effective anomaly detection. To address these limitations, we propose an efficient personalized federated PCA (FedEP) method for anomaly detection in IoT networks. The proposed model achieves personalization through introducing local representations with the $\ell_1$-norm for element-wise sparsity, while maintaining robustness via enforcing local models with the $\ell_{2,1}$-norm for row-wise sparsity. To solve this non-convex problem, we develop a manifold optimization algorithm based on the alternating direction method of multipliers (ADMM) with rigorous theoretical convergence guarantees. Experimental results confirm that the proposed FedEP outperforms the state...
