Weakly Supervised anomaly detection with privacy preservation under a Bi-Level Federated learning framework

Training a Machine Learning (ML) model in the industrial field faces special challenges, such as data privacy, lack of data, data imbalance, and unlabeled data. Therefore, it is not realistic to gather production data directly from various companies and use them to train a machine learning model. In this paper, we proposed a novel framework named Bi-level Federated Learning (BFL) to tackle the above challenges. In the first level, a Weakly Supervised Anomaly Detection method named Pairwise Relation prediction-based Ordinal regression Network (PRO) is utilized for training a Deep Anomaly Detection (DAD) model under Federated Learning (FL) mechanism. According to the DAD model, anomalies are identified and labeled with ‘anomaly’ tags. In order to train a multi-classification model that can identify different types of defects not just anomalies, the anomalies are manually labeled according to their respective defect types. In the second level, labeled anomaly and normal data are applied to train a multi-classification model under the FL mechanism. A framework is proposed to classify defects in pre-baked carbon anodes. Its performance is verified through a real case study. The results show that the DAD model achieved an acceptable performance with a 0.988 recall and 0.866 F1 score. The multi-classification model has shown good performance on 0.942 accuracy, 0.913 precision, 0.903 recall, and 0.906 F1. The results of the BFL exhibit improved performance compared to our previous work and classical ML methods.