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.

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