# Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework

arXiv: https://arxiv.org/abs/2211.01827 · HTML: https://mlsanity.com/paper/2211.01827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)

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

- [FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments](https://mlsanity.com/paper/2305.08504/) (2023)

## Abstract (arXiv)

This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new sensors with minimal online reconfiguration. Our framework currently supports multiple drift estimators for time-series IoT data and can easily be extended to accommodate new data types and drift detection mechanisms. This demo will illustrate the functionality of LE3D under a real-world-like scenario.