mlsanityData to 7 Oct 2026

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

arXiv 2211.01827 ↗First posted 3 Nov 2022cs.LG cs.CR cs.SEcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

3 of 9 references.

References · 3

  1. 2020River: machine learning for streaming data in Pythoncited by 96
  2. 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 82
  3. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deploymentscited by 2