# Global Outlier Detection in a Federated Learning Setting with Isolation Forest

arXiv: https://arxiv.org/abs/2409.13466 · HTML: https://mlsanity.com/paper/2409.13466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning](https://mlsanity.com/paper/2105.10545/) (2021)

## Abstract (arXiv)

We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two servers and the transmission of masked local data from clients to one of the servers. The masking of the data prevents the disclosure of sensitive information while still permitting the identification of outliers. Moreover, to further safeguard privacy, a permutation mechanism is implemented so that the server does not know which client owns any masked data point. The server performs outlier detection on the masked data, using either Isolation Forest or its extended version, and then communicates outlier information back to the clients, allowing them to identify and remove outliers in their local datasets before starting any subsequent federated model training. This approach provides comparable results to a centralized execution of Isolation Forest algorithms on plain data.