FedLGAN: a method for anomaly detection and repair of hydrological telemetry data based on federated learning.

ABSTRACT: The existing data repair methods primarily focus on addressing missing data issues by utilizing variational autoencoders to learn the underlying distribution and generate content that represents the missing parts, thus achieving data repair. However, this method is only applicable to data missing problems and cannot identify abnormal data. Additionally, as data privacy concerns continue to gain public attention, it poses a challenge to traditional methods. This article proposes a generative adversarial network (GAN) model based on the federated learning framework and a long short-term memory network, namely the FedLGAN model, to achieve anomaly detection and repair of hydrological telemetry data. In this model, the discriminator in the GAN structure is employed for anomaly detection, while

SUBMITTER: Chen Z 

PROVIDER: S-EPMC10703019 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature