Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train local ML model at each source and at the central location combine all the local models to generate a global model. In this work, we develop a proof-of-concept of distributed machine learning model, federated transfer learning, using a multi-kernel-based adaptive support vector machine. For federated learning, the multikernel approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.
How to Cite
Federated learning, Transfer learning, Support Vector Machine, Kernel, Machine learning
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