Differential Privacy Integrated Federated Learning for Power Systems: An Explainability-Driven Approach

Abstract

With the ongoing digitalization and intelligence of power systems, there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting. Nevertheless, power data often contains sensitive information, making it a critical industry challenge to efficiently utilize this data while ensuring privacy. Traditional Federated Learning (FL) methods can mitigate data leakage by training models locally instead of transmitting raw data. Despite this, FL still has privacy concerns, especially gradient leakage, which might expose users’ sensitive information. Therefore, integrating Differential Privacy (DP) techniques is essential for stronger privacy protection. Even so, the noise from DP may reduce the performance of federated learning models. To address this challenge, this paper presents an explainability-driven power data privacy federated learning framework. It incorporates DP technology and, based on model explainability, adaptively adjusts privacy budget allocation and model aggregation, thus balancing privacy protection and model performance. The key innovations of this paper are as follows: (1) We propose an explainability-driven power data privacy federated learning framework. (2) We detail a privacy budget allocation strategy: assigning budgets per training round by gradient effectiveness and at model granularity by layer importance. (3) We design a weighted aggregation strategy that considers the SHAP value and model accuracy for quality knowledge sharing. (4) Experiments show the proposed framework outperforms traditional methods in balancing privacy protection and model performance in power load forecasting tasks.