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October 11, 2025Open Access

Achieving Robustness and Dropout Fairness with Hierarchical Federated Learning in Smart Grid Infrastructures

Key Points

The proposed SGHFL framework significantly improves dropout resilience and robustness against data poisoning.
Evaluation across three datasets demonstrates the effectiveness of the SGHFL framework in real-world smart grid applications.
The framework leverages lightweight models, catering to the limitations of devices in smart grid environments.
Theoretical justification supports the design and implementation of the SGHFL framework in existing smart grid architectures.

Abstract

With the recent rapid expansion of the smart grid infrastructure paving the way for greater integration of computer and network technologies within the power grid, it has become well-suited for the application of machine learning techniques. However, machine learning requires vast amounts of data, which within the smart grid setting can reveal great amounts of personal details of the individuals using the grid. This work considers the application of a variant of distributed machine learning, federated learning, which enhances data privacy. We propose a Smart Grid Hierarchical Federated Learning (SGHFL) framework, which is tuned to common smart grid architectures in the real-world. We demonstrate how our SGHFL framework improves client dropout and poisoning robustness, using relatively lightweight models suitable for devices with limited computational capability. We provide theoretical justification underlying our design, and have evaluated our algorithms and framework with three datasets/environments of progressively increasing practicality. We have also compared our framework with relevant works.

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