Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters
arXiv:2602.16181v1 Announce Type: new 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 performa
arXiv:2602.16181v1 Announce Type: new 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.
Executive Summary
This article proposes a federated learning framework for energy theft detection in smart grids, addressing both privacy and computational constraints. Leveraging a lightweight multilayer perceptron model and integrating basic differential privacy, the framework ensures formal privacy guarantees without compromising learning performance. Experimental results demonstrate competitive accuracy, precision, recall, and AUC scores. The proposed solution is practical and scalable for secure energy theft detection in next-generation smart grid infrastructures.
Key Points
- ▸ Federated learning framework for energy theft detection in smart grids
- ▸ Addressing both privacy and computational constraints
- ▸ Lightweight multilayer perceptron model for resource-constrained smart meters
- ▸ Basic differential privacy for formal privacy guarantees
- ▸ Competitive accuracy, precision, recall, and AUC scores in experimental results
Merits
Strength in Addressing Key Challenges
The proposed framework effectively addresses the key challenges of energy theft detection in smart grids, including privacy and computational constraints, making it a significant contribution to the field.
Impressive Experimental Results
The experimental results demonstrate competitive accuracy, precision, recall, and AUC scores, indicating the effectiveness of the proposed framework in real-world applications.
Demerits
Limited Evaluation on Non-IID Data
The article only evaluates the proposed framework on IID and non-IID data distributions, but the results may not generalize well to other scenarios, such as non-stationary data or data with concept drift.
Scalability and Deployment Considerations
The article does not discuss the scalability and deployment considerations of the proposed framework in large-scale smart grid infrastructures, which may be a concern for widespread adoption.
Expert Commentary
The article presents a novel federated learning framework for energy theft detection in smart grids, addressing key challenges in the field. The framework's use of a lightweight multilayer perceptron model and basic differential privacy is particularly noteworthy, as it ensures formal privacy guarantees without compromising learning performance. While the article's experimental results are impressive, further evaluation and testing are necessary to fully assess the framework's scalability and deployment considerations. The proposed framework has significant practical and policy implications, and its contributions can inform discussions on energy efficiency, sustainability, and cybersecurity in smart grids.
Recommendations
- ✓ Future research should focus on evaluating the proposed framework on more diverse and complex data scenarios, such as non-stationary data and data with concept drift.
- ✓ The framework's scalability and deployment considerations should be further investigated and addressed to ensure widespread adoption in smart grid infrastructures.
