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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances.

Maazen Alsabaan1, Abdelrhman Elsayed2, Atef Bondok3

  • 1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Federated Learning (FL) enables smart grid operators to collaboratively train Power Quality Disturbance (PQD) detection models without sharing sensitive data. A new defense mechanism protects these FL models against data poisoning attacks, enhancing grid security.

Keywords:
artificial intelligencefederated learningmachine learningpoisoning attackspower quality disturbancesmart grid

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Smart grids require advanced methods for detecting Power Quality Disturbances (PQDs).
  • Increased PQDs due to renewable energy and nonlinear loads necessitate robust detection.
  • Data privacy concerns limit the use of centralized deep learning for PQD classification.

Purpose of the Study:

  • To develop and evaluate Federated Learning (FL) based classifiers for PQD detection.
  • To assess the vulnerability of FL models to data poisoning attacks in PQD classification.
  • To implement and validate a defense mechanism against poisoning attacks in FL for PQDs.

Main Methods:

  • Developed FL-based classifiers for PQD detection and compared them to centralized models.
  • Emulated five data poisoning attack scenarios to evaluate model robustness.
  • Implemented a detection mechanism to identify and isolate malicious client updates.

Main Results:

  • FL models showed a slight performance decrease (97% to 96% accuracy) compared to centralized models.
  • Data poisoning attacks significantly reduced classifier accuracy.
  • The implemented defense mechanism effectively mitigated performance degradation from poisoned updates.

Conclusions:

  • FL offers a privacy-preserving approach for PQD classification in smart grids.
  • FL models are vulnerable to data poisoning attacks, impacting classification accuracy.
  • The proposed defense mechanism enhances the robustness of FL-based PQD classifiers against adversarial attacks.