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Related Experiment Video

Updated: Jun 6, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

PrivEdge: a hybrid split-federated learning framework for real-time electricity theft detection on edge nodes.

Ahmed Ramadan1, Marwa A Shouman2, Gamal Attiya2

  • 1Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin Elkom, Egypt.

Scientific Reports
|March 22, 2026
PubMed
Summary

PrivEdge, a novel Split-Federated Learning system, effectively detects electricity theft in real-time using edge devices. This approach enhances accuracy and privacy while reducing communication costs for smart grids.

Keywords:
Edge artificial intelligence (Edge AI)Electricity theft detectionPrivacy-preserving smart gridsSplit–federated learning

Related Experiment Videos

Last Updated: Jun 6, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Electricity theft causes significant non-technical losses in power grids.
  • Traditional detection methods face scalability, communication, and privacy challenges.

Purpose of the Study:

  • To introduce PrivEdge, a hybrid Split-Federated Learning (SL-FL) system for real-time electricity theft detection.
  • To enable privacy-aware detection on resource-constrained edge devices.

Main Methods:

  • Utilizes Raspberry Pi 4 smart meter gateways for localized preprocessing and lightweight LSTM FrontNets.
  • Employs server-side inference, collaborative coordination, ensemble stacking, and score-level fusion.
  • Integrates Split Learning for reduced communication and privacy preservation, alongside Federated Learning for non-IID data.
  • Incorporates secure aggregation and Laplace differential privacy (ε=3) for enhanced security, with optional homomorphic encryption.

Main Results:

  • PrivEdge outperforms centralized, FL-only, and SL-only frameworks in detection accuracy and F1-score, particularly under non-IID conditions.
  • Software assessment on Raspberry Pi 4 shows low inference time, consistent resource usage, and sustained performance.
  • Ablation studies validate the significance of localized preprocessing, temporal features, ensemble aggregation, and privacy-preserving learning.

Conclusions:

  • PrivEdge successfully bridges the gap between SL-FL concepts and practical deployment for privacy-aware electricity theft detection at the network edge.
  • The system offers a scalable, efficient, and privacy-preserving solution for smart grid security.