Importance-Aware Data Pre-Processing and Device Scheduling for Multi-Channel Edge Learning

Xiufeng Huang;Sheng Zhou
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Abstract

The large-scale deployment of intelligent Internet of things (IoT) devices have brought increasing needs for computation support in wireless access networks. Applying machine learning (ML) algorithms at the network edge, i.e., edge learning, requires efficient training, in order to adapt themselves to the varying environment. However, the transmission of the training data collected by devices requires huge wireless resources. To address this issue, we exploit the fact that data samples have different importance for training, and use an influence function to represent the importance. Based on the importance metric, we propose a data pre-processing scheme combining data filtering that reduces the size of dataset and data compression that removes redundant information. 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 training accuracy. Furthermore, we propose device scheduling policies, including rate-based and Monte-Carlo-based policies, for multi-device multi-channel systems, maximizing the summation of data importance of scheduled devices. Experiments show that the proposed device scheduling policies bring more than 2% improvement in training accuracy.
面向多通道边缘学习的重要感知数据预处理和设备调度
随着智能物联网设备的大规模部署,对无线接入网络的计算支持需求日益增长。在网络边缘应用机器学习(ML)算法,即边缘学习,需要有效的训练,以适应不断变化的环境。然而,设备采集的训练数据的传输需要巨大的无线资源。为了解决这个问题,我们利用数据样本对训练具有不同重要性的事实,并使用影响函数来表示重要性。基于重要性度量,我们提出了一种数据预处理方案,该方案结合了减少数据集大小的数据过滤和去除冗余信息的数据压缩。这样可以在保持训练精度的同时,大大减少数据样本的数量和每个数据样本的大小。此外,我们提出了多设备多通道系统的设备调度策略,包括基于速率和基于蒙特卡罗的策略,最大限度地提高了被调度设备的数据重要性总和。实验表明,所提出的设备调度策略使训练精度提高了2%以上。
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