  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.
