The detection of electricity theft is an important concern for both developed and developing economies to avoid financial loss and improve power distribution stability. Therefore, many automated solutions are proposed in terms of machine learning (ML) and deep learning (DL) for electricity theft detection (ETD) based on smart energy IoT data. However, these solutions do not consider the privacy of electricity consumers during ETD. Moreover, another overarching challenge in ETD is the predominant composition of honest users; therefore, models are often biased towards representing honest features. Therefore, a privacy-aware federated learning (FL) based solution is proposed in this paper for ETD. The proposed counter-bias FL-based solution employs a convolutional neural network (CNN), we call as FL-based CNN (FL-CNN), to detect the electricity theft consumers while using the Iot sourced electricity consumption (EC) data. The performance evaluation results state that the proposed FL-CNN outperforms state-of-the-art theft detection in terms of bandwidth requirement and reduces false theft attribution.


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    Titel :

    Privacy Preserving Energy-Aware Federated Learning Based Method for Energy Theft Detection


    Beteiligte:
    Nadeem, Zunaira (Autor:in) / Jaber, Mona (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    688042 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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