论文标题
一个有效的拆分微调框架，用于边缘和云协作学习
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
论文作者
论文摘要
为了使预训练的模型能够通过边缘设备上的本地数据进行微调，而无需与云共享数据，我们为边缘和云协作学习设计了有效的拆分微调（SFT）框架。在此框架中，我们提出了三种新技术。首先，我们提出了一种基于矩阵分解的方法，以压缩神经网络的中间输出，以减少边缘设备和云服务器之间的通信量。其次，我们消除了模型中的特定链接，而不会影响微调中的收敛性能。第三，我们在Pytorch上实施系统，以使用户可以轻松扩展其现有的培训脚本，以享受有效的优势和云协作学习。 9 NLP数据集的实验结果表明，我们的框架可以将通信流量减少96次，而对模型的准确性影响很小。
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.
