CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
<|reference_start|>CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community: 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 of knowledge transfer, referred to as Crowd Knowledge Transfer (CrowdTransfer), which aims to transfer prior knowledge learned from a crowd of agents to reduce the training cost and as well as improve the performance of the model in real-world complicated scenarios. Particularly, we present four transfer modes from the perspective of crowd intelligence, including derivation, sharing, evolution and fusion modes. Building upon conventional transfer learning methods, we further delve into advanced crowd knowledge transfer models from three perspectives for various AIoT applications. Furthermore, we explore some applications of AIoT areas, such as human activity recognition, urban computing, multi-robot system, and smart factory. Finally, we discuss the open issues and outline future research directions of knowledge transfer in AIoT community.<|reference_end|>
@article{liu2024crowdtransfer:,
    title={CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community},
    author={Yan Liu and Bin Guo and Nuo Li and Yasan Ding and Zhouyangzi Zhang and
  Zhiwen Yu},
    journal={arXiv preprint arXiv:2407.06485},
    year={2024},
    doi={10.1109/COMST.2024.3423319},
    archivePrefix={arXiv},
    eprint={2407.06485},
    primaryClass={cs.LG cs.AI}
}