# Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems

arXiv: https://arxiv.org/abs/2506.05138 · HTML: https://mlsanity.com/paper/2506.05138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Simple Python Testbed for Federated Learning Algorithms](https://mlsanity.com/paper/2305.20027/) (2023)
- [Correct orchestration of Federated Learning generic algorithms: formalisation and verification in CSP](https://mlsanity.com/paper/2306.14529/) (2023)
- [A Federated Learning Algorithms Development Paradigm](https://mlsanity.com/paper/2310.05102/) (2023)
- [MicroPython Testbed for Federated Learning Algorithms](https://mlsanity.com/paper/2405.09423/) (2024)

## Built on by (most cited first, 1 of 1)

- [Real-Time Machine Learning for Embedded Anomaly Detection](https://mlsanity.com/paper/2512.19383/) (2025)

## Abstract (arXiv)

Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low-resource, unsupervised method well-suited for edge deployment and continuous learning. In this paper, we present an application of Isolation Forest-based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 96% accuracy in distinguishing normal from abnormal readings and above 78% precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 160 KB during model training. These results highlight its suitability for resource-constrained environments and edge systems, while upholding federated learning principles of data privacy and collaborative learning.