%0 Journal Article %A WANG Haotian %A ZHENG Dongyi %A LIU Fang %A XIAO Nong %T Personalized Federated Anomaly Detection Method for Multivariate Time Series Data %D 2022 %R 10.3778/j.issn.1002-8331.2108-0057 %J Computer Engineering and Applications %P 60-65 %V 58 %N 11 %X With the wide application of real-time sensors in various fields such as airports, power plants, intelligent factories and health care systems, anomaly detection of multivariate time series data becomes more important. However, there are currently two key challenges. First of all, the sensitive data of data institutions usually exist in the form of isolated islands, which makes it difficult to fuse data under the premise of protecting privacy and can not train high-performance anomaly detection models. Secondly, the data of different data institutions are statistically heterogeneous. In the personalized data scenario, the performance of using a unified anomaly detection model is poor. This paper proposes a personalized federal anomaly detection framework FedPAD(federated personalized anomaly detection) for multivariate time series data. Based on the federal learning architecture, FedPAD aggregates data under the premise of protecting privacy, and then constructs a relatively personalized model by fine tuning. Experiments on NASA spacecraft dataset show that FedPAD can achieve accurate and personalized anomaly detection, and the average F1 score is increased by 6.9% compared with the benchmark method. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2108-0057