EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment
EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment
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DOI：
10.1145/3453142.3491286
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Pengzhan Hao;Yifan Zhang
中科院分区：
文献类型：
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作者：
Pengzhan Hao;Yifan Zhang
本文研究了在资源受限的嵌入式设备的边缘执行分布式深度学习（DAI）来训练机器学习（ML）模型的问题。现有的解决方案主要集中在数据中心环境中，其中功能强大的服务器级机器与超高速以太网互连，并且不适用于使用功能较弱的计算设备和网络的边缘环境。由于计算设备和连接它们的网络上的资源约束，执行基于边缘的训练存在三个主要挑战：（1）对挣扎的工人的敏感性，（2）难以扩展到大型训练集群，以及（3）训练设备可用性和能力的频繁变化。为了应对这些挑战，我们设计并实现了EDDL，一个基于边缘的嵌入式系统，基于ARM的ODROID-XU 4和树莓派3型号B板。我们通过在大型Android APK数据集上执行基于边缘的移动的恶意软件检测和分类来评估原型EDDL系统。评估结果表明，EDDL可以有效地训练消费级嵌入式设备和无线网络的深度学习模型，同时产生很小的开销。
This paper investigates the problem of performing distributed deep learning (DDL) to train machine learning (ML) models at the edge with resource-constrained embedded devices. Existing solutions mostly focus on data center environments, where powerful serverclass machines are interconnected with ultra-high-speed Ethernet, and are not suitable for edge environments where much less powerful computing devices and networks are used. Due to the resource constraint on computing devices and the network connecting them, there are three main challenges for performing edge-based DDL: (1) susceptibility to struggling workers, (2) difficulty of scaling up to a large training cluster, and (3) frequent changes in training device availability and capability. To address these challenges, we design and implement EDDL, an edge-based DDL system, with ARM-based ODROID-XU4 and Raspberry Pi 3 Model B boards. We evaluate the prototype EDDL system by performing edge-based mobile malware detection and classification on a large Android APK dataset. The evaluation results show that EDDL can efficiently train deep learning models with consumer-grade embedded devices and wireless networks while incurring small overhead.