Achieving democracy in edge intelligence

a fog-based collaborative learning scheme

Tiehua Zhang, Zhishu Shen, Jiong Jin, Xi Zheng, Atsushi Tagami, Xianghui Cao

Research output: Contribution to journalArticle


The emergence of fog computing has brought unprecedented opportunities to the Internet of Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this paper, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real-world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.
Original languageEnglish
JournalIEEE Internet of Things Journal
Early online date1 Sep 2020
Publication statusPublished - 2020

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