Nonlinear and Trend-Aware Industrial Time Series Anomaly Detection with Federated Learning
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Abstract
Industrial anomaly detection aims to identify significant data deviations. However, it is hampered by the complex dynamics of time series, distributed data silos, and data heterogeneity. To overcome these challenges, we introduce a novel federated learning framework (FL) with two core modules: Multiple Definition Operators (MDO) to capture intricate temporal dynamics, and Temporal Trend Convolution (TTC) to extract interpretable trend patterns. FL enables multiple clients to collaboratively train a robust global model without centralizing raw data, thereby boosting generalization and preserving privacy. Critically, a tailored data-sharing strategy is implemented within the framework to mitigate the challenge of non-independent and identically distributed data. Experiments conducted on the Skoltech Anomaly Benchmark and other real-world datasets validate the efficacy of the MDO and TTC modules as well as confirm that the proposed framework significantly improves anomaly detection performance, demonstrating its practical potential for industrial applications.
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Anomaly Detection, Federated Learning, Industrial Time Series
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