  To enable the pre-trained models to be fine-tuned with local data on edge
devices without sharing data with the cloud, we design an efficient split
fine-tuning (SFT) framework for edge and cloud collaborative learning. We
propose three novel techniques in this framework. First, we propose a matrix
decomposition-based method to compress the intermediate output of a neural
network to reduce the communication volume between the edge device and the
cloud server. Second, we eliminate particular links in the model without
affecting the convergence performance in fine-tuning. Third, we implement our
system atop PyTorch to allow users to easily extend their existing training
scripts to enjoy the efficient edge and cloud collaborative learning.
Experiments results on 9 NLP datasets show that our framework can reduce the
communication traffic by 96 times with little impact on the model accuracy.
