Distributed Federated Autoencoder-DeepCNN Framework for False Data Injection Attack Detection in Consumer Applications

Rapid advancements in urbanization and industrialization have increased consumer-end applications in smart grids, where advanced data-driven methods have significantly improved consumer-level energy management using smart meters. However, these systems are highly vulnerable to cyberattacks, notably false data injection attacks (FDIA), which can compromise grid reliability and stability. Existing detection techniques often rely on centralized data collection and processing, which raises substantial privacy concerns and leads to overfitting due to limited data diversity. To address these issues, this paper presents a novel framework that integrates federated learning with an autoencoder-based 1D deep convolutional neural network (AE-1DdeepCNN) for FDIA in consumer applications. The proposed framework enables collaborative model training based on the IEEE-14 bus system dataset for anomaly detection while ensuring data privacy. Extensive experimental results with row-accuracy of 93.35%~99.40% in different smart meters demonstrate the effectiveness and robustness of the proposed method for FDIA detection in smart grid systems and consumer smart metering applications.