CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
Yan Liu, Bin Guo, Nuo Li, Yasan Ding, Zhouyangzi Zhang and, Zhiwen Yu

TL;DR
This paper introduces CrowdTransfer, a novel knowledge transfer approach leveraging crowd intelligence to enhance AIoT applications by reducing training costs and improving model performance in complex scenarios.
Contribution
It proposes the concept of Crowd Knowledge Transfer, presents four transfer modes from crowd intelligence, and explores advanced models and applications in AIoT.
Findings
Four transfer modes: derivation, sharing, evolution, fusion.
Enhanced AIoT performance through crowd knowledge transfer.
Discussion of open issues and future research directions.
Abstract
Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of Internet of Things (IoT) and Artificial Intelligence (AI) technologies. Although advanced deep learning techniques enhance the efficient data processing and intelligent analysis of complex IoT data, they still suffer from notable challenges when deployed to practical AIoT applications, such as constrained resources, and diverse task requirements. Knowledge transfer is an effective method to enhance learning performance by avoiding the exorbitant costs associated with data recollection and model retraining. Notably, although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances of various knowledge transfer techniques for AIoT field. This survey endeavors to introduce a new concept…
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