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The generalization ability of supervised model is relatively weak in keyword extraction technology. In order to effectively improve the robustness and accuracy of the model, the key to overcome this problem is collect more data for training process. However, the text as an privacy information, it is harder and harder to collect. To solve this problem, we apply federal learning to use user privacy data locally to improve the keyword extraction performance. In order to integrate the unlabeled user utterances to the semantic parsing model, we proposed a Pre-training and multi-task based federated keyword extraction model. The user models learn local information via unsupervised setting and the core model learns the supervised setting.

Pre-training and multi-task training for federated keyword extraction