Adaptive Transmission for Edge Learning via Training Loss Estimation

With the large-scale deployment of intelligent Internet of things (IoT) devices and the increasing need for computation support in wireless access networks, edge computing plays a vital role in satisfying these merging needs. The deployment of machine learning algorithms as one of the key applications at the network edge requires efficient training, in order to adapt themselves in the changing environment. However, the transmission of the training dataset collected by edge devices requires huge wireless communication resources. To address this issue, we exploit the fact that data samples have different importance for training, and use the training loss of the target machine learning model to represent the importance of data samples. Based on the importance metric, we propose a data upload scheme combining data compression that removes unimportant information and data filtering for data selection. As a result, the number of data samples as well as the size of every data sample to be transmitted can be substantially reduced while keeping the good training performance. Experiments show that training a machine learning model via the proposed scheme can enjoy faster convergence under limited wireless resources, specifically, we get almost the same training performance with only 2.5% of communication resources in the experiments training on MNIST dataset.