Dynamic Data Collection and Neural Architecture Search for Wireless Edge Intelligence Systems

Abstract

With the booming development of Internet of things (IoT) devices and machine learning (ML) technique, edge machine learning is emerging to process the enormous sampled data for realizing intelligent applications at the network edge. With limited edge resources, a well-structured neural network and numerous training data are the two main factors that affect the performance of edge machine learning. In this paper, we cooperatively optimize the data collection and the neural architecture to minimize the energy consumption of devices and the error on a specific task. We derive the Rademacher complexity bounds theoretically to evaluate the generalization error of the neural architectures in the search space and then formulate the optimization problem accordingly. Then we develop a scheme to solve the problem that dynamically performs the data collection based on policy gradient reinforcement learning and the parameter-sharing neural architecture search (NAS) algorithm. By this way, the transmission power of each device can be adjusted based on the data quality assessed by the NAS result in each round to effectively collect data. And with the growing high-quality data, the NAS algorithm can gradually find the optimal architecture for the task. Experimental results show that the neural architectures found by the proposed algorithm outperform the existing architectures while saving energy in the device.