# Federated PCA on Grassmann Manifold for IoT Anomaly Detection

arXiv: https://arxiv.org/abs/2407.07421 · HTML: https://mlsanity.com/paper/2407.07421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 30 of 30)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems](https://mlsanity.com/paper/1410.1390/) (2014)
- [Building Deep Networks on Grassmann Manifolds](https://mlsanity.com/paper/1611.05742/) (2016)
- [Generalized Low Rank Models](https://mlsanity.com/paper/1410.0342/) (2014)
- [Communication-Efficient Adaptive Federated Learning](https://mlsanity.com/paper/2205.02719/) (2022)
- [Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning](https://mlsanity.com/paper/2012.08677/) (2020)
- [Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms](https://mlsanity.com/paper/1806.02246/) (2018)
- [Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning](https://mlsanity.com/paper/1808.02229/) (2018)
- [Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things](https://mlsanity.com/paper/1911.05771/) (2019)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning](https://mlsanity.com/paper/1911.03654/) (2019)
- [Distributed Convex Optimization with Many Convex Constraints](https://mlsanity.com/paper/1610.02967/) (2016)
- [Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2212.12121/) (2022)
- [Locality Preserving Projections for Grassmann manifold](https://mlsanity.com/paper/1704.08458/) (2017)
- [Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds](https://mlsanity.com/paper/2010.08908/) (2020)
- [Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance](https://mlsanity.com/paper/1509.02604/) (2015)

## Built on by (most cited first, 3 of 3)

- [A Secure and Private Distributed Bayesian Federated Learning Design](https://mlsanity.com/paper/2602.20003/) (2026)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection](https://mlsanity.com/paper/2602.12622/) (2026)

## Abstract (arXiv)

With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection systems (ML-IDS) effectively employ supervised learning methods, they possess limitations such as the requirement for labeled data and challenges with high dimensionality. Recent unsupervised ML-IDS approaches such as AutoEncoders and Generative Adversarial Networks (GAN) offer alternative solutions but pose challenges in deployment onto resource-constrained IoT devices and in interpretability. To address these concerns, this paper proposes a novel federated unsupervised anomaly detection framework, FedPCA, that leverages Principal Component Analysis (PCA) and the Alternating Directions Method Multipliers (ADMM) to learn common representations of distributed non-i.i.d. datasets. Building on the FedPCA framework, we propose two algorithms, FEDPE in Euclidean space and FEDPG on Grassmann manifolds. Our approach enables real-time threat detection and mitigation at the device level, enhancing network resilience while ensuring privacy. Moreover, the proposed algorithms are accompanied by theoretical convergence rates even under a subsampling scheme, a novel result. Experimental results on the UNSW-NB15 and TON-IoT datasets show that our proposed methods offer performance in anomaly detection comparable to nonlinear baselines, while providing significant improvements in communication and memory efficiency, underscoring their potential for securing IoT networks.