Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters

TL;DR

This paper introduces a federated learning framework using lightweight models and differential privacy to detect energy theft securely and efficiently in resource-constrained smart meters, ensuring privacy without sacrificing accuracy.

Contribution

It presents a novel federated learning approach with privacy guarantees tailored for resource-limited smart meters in smart grids, addressing privacy and computational challenges.

Findings

Achieves competitive detection accuracy and metrics.

Maintains privacy through differential privacy mechanisms.

Demonstrates scalability and practicality in real-world datasets.

Abstract

Energy theft poses a significant threat to the stability and efficiency of smart grids, leading to substantial economic losses and operational challenges. Traditional centralized machine learning approaches for theft detection require aggregating user data, raising serious concerns about privacy and data security. These issues are further exacerbated in smart meter environments, where devices are often resource-constrained and lack the capacity to run heavy models. In this work, we propose a privacy-preserving federated learning framework for energy theft detection that addresses both privacy and computational constraints. Our approach leverages a lightweight multilayer perceptron (MLP) model, suitable for deployment on low-power smart meters, and integrates basic differential privacy (DP) by injecting Gaussian noise into local model updates before aggregation. This ensures formal privacy guarantees without compromising learning performance. We evaluate our framework on a real-world smart meter dataset under both IID and non-IID data distributions. Experimental results demonstrate that our method achieves competitive accuracy, precision, recall, and AUC scores while maintaining privacy and efficiency. This makes the proposed solution practical and scalable for secure energy theft detection in next-generation smart grid infrastructures.