Edge computing is emerging as a new infrastructure for Internet-of-Things (IoT) networks by placing computation and analytics near to where data are generated. This article presents a novel data analytics framework for edge computing. The framework is based on a new decentralized algorithm, which enables all the nodes to obtain the global optimal model without sharing raw data. The resulting scheme executes in a hybrid mode: local IoT nodes send computed information to edge nodes. The edge nodes cooperate with each other by exchanging analytics with their neighbors only. The presenting approach is analyzed and evaluated on various applications and the experimental results demonstrate the effectiveness of the proposed methodology in providing fast data analytics to edge computing infrastructure.
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Zhao et al. (2020) studied this question.
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