The exponential growth of Internet of Things (IoT) has become a transcending force in creating innovative smart devices and connected domains including smart homes, healthcare, transportation and manufacturing. With billions of IoT devices, there is a huge amount of data continuously being generated, transmitted, and stored at various points in the IoT architecture. Deep learning is widely being used in IoT applications to extract useful insights from IoT data. However, IoT users have security and privacy concerns and prefer not to share their personal data with third party applications or stakeholders. In order to address user privacy concerns, Collaborative Deep Learning (CDL) has been largely employed in data-driven applications which enables multiple IoT devices to train their models locally on edge gateways. In this chapter, we first discuss different types of deep learning approaches and how these approaches can be employed in the IoT domain. We present a privacy-preserving collaborative deep learning approach for IoT devices which can achieve benefits from other devices in the system. This learning approach is analyzed from the behavioral perspective of mobile edge devices using a game-theoretic model. We analyze the Nash Equilibrium in N-player static game model. We further present a novel fair collaboration strategy among edge IoT devices using cluster based approach to solve the CDL game, which enforces mobile edge devices for cooperation. We also present implementation details and evaluation analysis in a real-world smart home deployment.
翻译:在创建创新智能装置和包括智能家庭、保健、交通和制造等相关领域方面,Tings Internet Internet Internet(IoT)的指数增长已成为一种超越力量,在创建创新智能装置和包括智能家庭、保健、交通和制造等相关领域方面超越了力量。由于有数十亿 IoT 设备,大量的数据在IoT 结构的各个点不断生成、传输和储存。在IoT 应用中广泛使用深度学习,从 IoT 数据中获取有用的洞察。然而,IoT 用户有安全和隐私方面的关切,不愿与第三方应用程序或利益攸关方分享个人数据。为了解决用户隐私问题,合作深层学习(CDL)主要用于数据驱动的应用,使多个IoT 设备能够在当地边缘网关上培训模型。在本章中,我们首先讨论不同种类的深层学习方法,以及如何在IoT 域中应用这些方法来获取有用的洞察力。我们用游戏模型模型从移动边缘设备的行为视角来分析我们目前的Sequilibribrial 移动游戏游戏的游戏游戏游戏模型的Sloveal Cloveal coal roupal coal coal coal roupal roupal coal laction laction CD-real laction laction laction lactionmentmental lactionmental lactionmental lactionmental lactionmentmentmentmental lactionmental laction lactionmentmentmentmentmentmentmental lactionmental laction.我们用的是,我们用NCreal CD-to CD-h