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A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection

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Listed:
  • Wen, Hanguan
  • Liu, Xiufeng
  • Lei, Bo
  • Yang, Ming
  • Cheng, Xu
  • Chen, Zhe

Abstract

Electricity theft is a critical issue in smart grids, leading to significant financial losses for utilities and compromising the stability and reliability of the power system. Existing centralized methods for electricity theft detection raise privacy and security concerns due to the need for sharing sensitive customer data. To address these challenges, we propose HeteroFL, a novel heterogeneous federated learning framework for privacy-preserving electricity theft detection in smart grids. HeteroFL enables retailers to collaboratively train a global model without sharing their private data, while accounting for the class imbalance problem prevalent in electricity theft datasets. We introduce a data partitioning and aggregation scheme that assigns different weights to classes, ensuring a balanced contribution and representation of each class in the global model. In addition, our framework leverages the CKKS homomorphic encryption scheme to perform secure computations on encrypted parameters and employs a CNN-LSTM model to capture the spatial and temporal dependencies in electricity consumption patterns. We evaluate HeteroFL using a real-world smart grid dataset and demonstrate its effectiveness and efficiency in detecting energy theft. Furthermore, we analyze the robustness and perform ablation studies to validate the framework’s stability and identify the contributions of its key components. Although the impact of approximation errors introduced by the CKKS scheme on the CNN-LSTM model’s performance requires further investigation, our framework presents a promising solution for privacy-preserving and accurate electricity theft detection in smart grids using heterogeneous federated learning.

Suggested Citation

  • Wen, Hanguan & Liu, Xiufeng & Lei, Bo & Yang, Ming & Cheng, Xu & Chen, Zhe, 2025. "A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection," Applied Energy, Elsevier, vol. 378(PA).
  • Handle: RePEc:eee:appene:v:378:y:2025:i:pa:s030626192402172x
    DOI: 10.1016/j.apenergy.2024.124789
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    References listed on IDEAS

    as
    1. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Cheng, Xu & Chen, Zhe, 2023. "An energy demand-side management and net metering decision framework," Energy, Elsevier, vol. 271(C).
    2. ., 2022. "The Federal Reserve and the Great Depression," Chapters, in: A Comparative History of Central Bank Behavior, chapter 7, pages 159-191, Edward Elgar Publishing.
    3. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    4. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
    5. Muhammad Mansoor Ashraf & Muhammad Waqas & Ghulam Abbas & Thar Baker & Ziaul Haq Abbas & Hisham Alasmary, 2022. "FedDP: A Privacy-Protecting Theft Detection Scheme in Smart Grids Using Federated Learning," Energies, MDPI, vol. 15(17), pages 1-15, August.
    6. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    7. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    8. Li, Zhengmao & Xu, Yan & Wang, Peng & Xiao, Gaoxi, 2023. "Coordinated preparation and recovery of a post-disaster Multi-energy distribution system considering thermal inertia and diverse uncertainties," Applied Energy, Elsevier, vol. 336(C).
    9. ., 2022. "The multiple faces of federal government," Chapters, in: Rethinking Public Choice, chapter 8, pages 101-113, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

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