# Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection

arXiv: https://arxiv.org/abs/2602.12622 · HTML: https://mlsanity.com/paper/2602.12622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Anomaly Detection by Robust Statistics](https://mlsanity.com/paper/1707.09752/) (2017)
- [Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data](https://mlsanity.com/paper/2001.05484/) (2020)
- [Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey](https://mlsanity.com/paper/2503.13195/) (2025)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT](https://mlsanity.com/paper/2110.14937/) (2021)
- [Bi-Sparse Unsupervised Feature Selection](https://mlsanity.com/paper/2412.16819/) (2024)
- [A Regularized Semi-Smooth Newton Method With Projection Steps for Composite Convex Programs](https://mlsanity.com/paper/1603.07870/) (2016)
- [Federated PCA on Grassmann Manifold for IoT Anomaly Detection](https://mlsanity.com/paper/2407.07421/) (2024)
- [Sparse Tensor CCA via Manifold Optimization for Multi-View Learning](https://mlsanity.com/paper/2504.02339/) (2025)

## Abstract (arXiv)

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 ℓ1-norm for element-wise sparsity, while maintaining robustness via enforcing local models with the ℓ2,1-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-of-the-art FedPG, achieving excellent F1-scores and accuracy in various IoT security scenarios. Our code will be available at hrefhttps://github.com/xianchaoxiu/FedEPhttps://github.com/xianchaoxiu/FedEP.