FitCNN: A cloud-assisted and low-cost framework for updating CNNs on IoT devices

Abstract Recently convolutional neural networks (CNNs) have essentially achieved the state-of-the-art accuracies in image classification and recognition tasks. CNNs are usually deployed in the cloud to handle data collected from IoT devices, such as smartphones and unmanned systems. However, significant data transmission overhead and privacy issues have made it necessary to use CNNs directly in device side. Nevertheless, the trained model deployed on mobile devices cannot effectively handle the unknown data and objects in new environments, which could lead to low accuracy and poor user experience. Hence, it would be crucial to re-train a better model via future unknown data. However, with tremendous computing cost and memory usage, training a CNN on IoT devices with limited hardware resources is intolerable in practice. To solve this issue, using the power of cloud to assist mobile devices to train a deep neural network becomes a promising solution . Therefore, this paper proposes a cloud-assisted CNN framework, named FitCNN, with incremental learning and low data transmission, to reduce the overhead of updating CNNs deployed on devices. To reduce the data transmission during incremental learning, we propose a strategy, called Distiller, to selectively upload the data that is worth learning, and develop an extracting strategy, called Juicer, to choose light amount of weights from the new CNN model generated on the cloud to update the corresponding old ones on devices. Experimental results show that the Distiller strategy can reduce 39.4% data transmission of uploading based on a certain dataset, and the Juicer strategy reduces by more than 60% data transmission of updating with multiple CNNs and datasets.