Efficient XAI-based Federated Learning Approach for Accurate Detection of False Data in Smart Grid

A major challenge in smart power grids (SGs) is the injection of false data by malicious actors, which may disrupt the proper operation and cause financial losses. To detect such false data, machine learning (ML)-based methods are commonly used, with models being trained through either centralized learning (CL) or federated learning (FL) approaches. While CL approach achieves higher classification accuracy, it introduces significant privacy risks. In contrast, the existing FL approaches mitigate privacy concerns by keeping data decentralized but suffer from lower classification accuracy compared to CT approach in addition to high overhead in terms of communication. To address these limitations, we propose an explainable artificial intelligence (XAI)-based FL approach that is trained on the explanations of electricity power consumption readings instead of the readings themselves. This approach not only improves false data detection accuracy but also significantly reduces FL communication overhead. The idea is that by leveraging XAI, our approach can effectively distinguish between benign and malicious samples, enabling the establishment of a precise decision boundary with fewer training rounds. To verify our approach, we conducted extensive evaluations using two real-world -electricity consumption datasets under various cyber-attack scenarios. The experimental results demonstrate that our XAI-based FL approach outperforms the existing data-based FL approaches, achieving superior accuracy and reduced overhead. For instance, in some cases, our approach was able to reduce the number of FL rounds by more than 80%, reduce the false alarm by more than 82%, and increase the detection accuracy by more than 14%.