With the growth of Internet of Things (IoT) and mo-bile edge computing, billions of smart devices are interconnected to develop applications used in various domains including smart homes, healthcare and smart manufacturing. Deep learning has been extensively utilized in various IoT applications which require huge amount of data for model training. Due to privacy requirements, smart IoT devices do not release data to a remote third party for their use. To overcome this problem, collaborative approach to deep learning, also known as Collaborative DeepLearning (CDL) has been largely employed in data-driven applications. This approach enables multiple edge IoT devices to train their models locally on mobile edge devices. In this paper,we address IoT device training problem in CDL by analyzing the behavior of mobile edge devices using a game-theoretic model,where each mobile edge device aims at maximizing the accuracy of its local model at the same time limiting the overhead of participating in CDL. We analyze the Nash Equilibrium in anN-player static game model. We further present a novel cluster-based fair strategy to approximately solve the CDL game to enforce mobile edge devices for cooperation. Our experimental results and evaluation analysis in a real-world smart home deployment show that 80% mobile edge devices are ready to cooperate in CDL, while 20% of them do not train their local models collaboratively.
翻译:随着Tings Internet(IoT)和mo-bile 边缘计算的增长,数十亿智能设备被连接起来,用于开发各种领域的应用应用,包括智能家庭、医疗保健和智能制造。深入学习被广泛用于需要大量数据用于模型培训的各种 IoT 应用中。由于隐私要求,智能 IoT 设备不会向边远第三方发布数据供其使用。为了解决这一问题,在数据驱动的应用程序中主要采用了称为Cooperation DeepLearning(Cooperation DeepLearning(CDL)的深层次学习合作方法。这个方法使得多边缘IoT 设备能够在移动边缘设备上培训其模型。在本文中,我们通过使用游戏理论模型分析移动边缘设备的行为,在CDL 中解决I 设备的培训问题。我们每个移动边缘设备的目的是在限制其本地模型参与CDL 的间接费用时,最大限度地提高本地模型的准确性。我们用在N-Player静态游戏模型中分析了纳什· Equilibrium(CL) 。我们进一步提出了一个基于集群的公平战略,以大致解决CDL 游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏游戏,以便在智能边缘设备上实施移动边缘设备,在智能部署80的移动边缘设备上,我们实验后,我们的软化的实验结果分析结果和实验性计算机上显示的20的模型,在移动式的实验性软的模型,我们的实验性实验性实验性DL 。