Federated Episodic Learning to Extrapolate Unseen from Seen Conditions for Industrial IoT Monitoring

Online monitoring is essential for the safety of Industrial IoT (IIoT). Most existing methods seek low-dimensional representations to assess the overall operation status. However, we reveal that the existing methods face some unsolved and interrelated limitations, including coarse granularity, tight boundary, and weak extrapolation. This article proposes a federated episodic learning method for IIoT monitoring that simultaneously enhances interpretability, robustness, and extrapolation. The method centers on a dual-level normality bank with a normality contrastive separation network and an episodic training strategy, designed within a cloud-edge collaborative manner. To solve the coarse granularity issue, we propose a dual-level normality bank from both condition-level and variable-level perspectives, which facilitates fine-grained pattern matching and improves interpretability. To address the tight boundary issue, we propose a normality contrastive separation network, which utilizes prior fault knowledge to construct negative samples and encourages models to focus on fault-related representations, thus improving robustness. To tackle the weak extrapolation issue, we design an episodic training strategy, which develops a client alternation policy to construct refining sets and makes inferences using patterns from adjacent working conditions. It fully exploits the relation of adjacent working conditions and improves extrapolation for unseen conditions with theoretical guarantees. Extensive experiments on two clusters validate the method's superior interpretability, robustness, and extrapolation.