Collaborative edge and cloud learning: Potentials and solutions

协作边缘和云学习：潜力和解决方案

• 批准号: 522129-2017
• 负责人: Liu, Jiangchuan
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640426
• 关键词: Collaborative; edge; cloud; learning; Potentials

The data generated and contributed by today's personal computing devices, ranging from text, voice, to pictureand video, are enormous and contain invaluable information worth discovering. Recent advances in deep neuralnetworking have shown great potentials in exploring the hidden information therein. Deep learning relies onstrong computation power to process the massive amount of data, which is typical offered by machine clusters,or more general, modern data centers. While cloud-centric learning works well for the data that are available indatacenters, gathering the data from worldwide sources unavoidably incurs high traffic and, more importantly,latency that challenges such realtime learning applications as face recognition and human tracking in cameranetworks. The concept of edge computing has been recently advocated as a complement to cloud computing. Itpushes applications, data, and computing content away from the centralized data centers. As such, it cansignificantly accelerate the training process by reducing the traffic transferred to the cloud and the inferencelatency for a broad spectrum of deep learning applicationsHuawei is an industrial pioneer in building the communication and computation infrastructure for edgecomputing, and has started building its public cloud infrastructure as well. In this project, we will worktogether to understand the state-of-the-art of collaborative edge and cloud learning. We will identify theopportunities and challenges on how to push data pre-processing and feature extracting to the network edge.We will then develop novel solutions that minimize the network traffic and inference latency yet with desiredaccuracy for deep learning.

当今的个人计算设备生成和提供的数据（从文本、语音到图片和视频）数量巨大，并且包含值得发现的宝贵信息。深度神经网络的最新进展显示出探索其中隐藏信息的巨大潜力。深度学习依靠强大的计算能力来处理大量数据，这通常由机器集群或更普遍的现代数据中心提供。虽然以云为中心的学习对于数据中心中的可用数据效果很好，但从全球来源收集数据不可避免地会产生高流量，更重要的是，延迟会对摄像头网络中的人脸识别和人体跟踪等实时学习应用提出挑战。边缘计算的概念最近被提倡作为云计算的补充。它将应用程序、数据和计算内容推离集中式数据中心。因此，它可以通过减少传输到云端的流量和广泛的深度学习应用的推理延迟来显着加速训练过程华为是构建边缘计算通信和计算基础设施的行业先驱，并已开始构建其公共云基础设施以及。在这个项目中，我们将共同努力了解协作边缘和云学习的最新技术。我们将确定如何将数据预处理和特征提取推至网络边缘的机遇和挑战。然后，我们将开发新颖的解决方案，最大限度地减少网络流量和推理延迟，同时达到深度学习所需的准确性。