Cloud-edge collaborative natural language processing method based on lightweight transfer learning
A lightweight transfer module was introduced to re solve the problem that current pre-trained language models (PLMs) cannot be operated and trained on edge devices due to the excessive number of parameters.The deployment of the transfer module was separated from the large PLM,and an efficient cloud-side collaborative transfer learning framework was implemented,which could transfer PLM to downstream tasks with only a small number of parameters fine-tuned.Cross-domain cloud-side collaborative deployment was also supported.Downstream tasks in multiple domain can collaboratively share the same PLM,which effectively saves computing overhead.Tasks can be efficiently separated and deployed on different devices to realize the separate deployment of multiple tasks and the sharing of PLM.Experiments on four public natural language processing task datasets were conducted,and the results showed that the performance of this framework was over 95％ of that of fully fine-tuned BERT methods.
natural language processingtransfer learningcloud-edge collaborationcomputation efficiencymodel deployment
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