Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning.

ABSTRACT: The growing use of Internet-of-Things devices in electric power systems has resulted in increased complexity and flexibility, making monitoring power usage critical for effective system maintenance and detecting abnormal behavior. However, traditional anomalous power consumption detection methods struggle to handle the vast amounts of data generated by these devices. While deep learning and machine learning are effective in anomaly detection, they require significant amounts of training data collected on centralized servers. This centralized approach results in high response time delays and data leakage problems. To address these challenges, we propose an Autoencoder-based Federated Learning method that combines the AutoEncoder and Federated Learning networks to develop a high-accuracy alg

SUBMITTER: Kea K 

PROVIDER: S-EPMC10437833 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature