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    面向AIoT的协同智能综述

    Survey of AIoT-Oriented Collaborative Intelligence

    • 摘要: 深度学习和物联网的融合发展有力地促进了AIoT生态的繁荣. 一方面AIoT设备为深度学习提供了海量数据资源,另一方面深度学习使得AIoT设备更加智能化. 为保护用户数据隐私和克服单个AIoT设备的资源瓶颈,联邦学习和协同推理成为了深度学习在AIoT应用场景中广泛应用的重要支撑. 联邦学习能在保护隐私的前提下有效利用用户的数据资源来训练深度学习模型,协同推理能借助多个设备的计算资源来提升推理的性能. 引入了面向AIoT的协同智能的基本概念,围绕实现高效、安全的知识传递与算力供给,总结了近10年来联邦学习和协同推理算法、架构和隐私安全3个方面的相关技术进展,介绍了联邦学习和协同推理在AIoT应用场景中的内在联系. 从设备共用、模型共用、隐私安全机制协同和激励机制协同等方面展望了面向AIoT的协同智能的未来发展.

       

      Abstract: The fusion of deep learning and the Internet of Things has significantly promoted the development of the AIoT ecosystem. On the one hand, the huge amounts of multi-modal data collected by AIoT devices provide deep learning with abundant training data resources, which plays a more important role in the era of big models. On the other hand, the development of deep learning makes AIoT devices smarter, which shows great potential for promoting social development and the convenience of human life. As major support for the usage of deep learning in AIoT, federated learning effectively makes use of the training data provided by AIoT devices to train deep learning models with data privacy protection while collaborative inference overcomes the obstacles in the deployment of deep learning brought by the limited computation resource of AIoT devices. We induce the concept of AIoT-oriented collaborative intelligence. Aiming at implementing knowledge transmission and computation resource supply with high efficiency and security, we review the related works, published in the past 10 years, about the architecture, algorithm, privacy, and security of federated learning and collaborative inference, and introduce the inner connection of federated learning and collaborative inference. The algorithm part summarizes the federated learning and collaborative inference algorithm related to AIoT use cases and their optimization goals. The architecture part introduces the related works about deep learning accelerators, deep learning compilation, deep learning frameworks, communication among devices, and collaboration among devices from the view of AI computing systems. The privacy and security part introduces the privacy and security threats faced by AIoT-oriented collaborative intelligence and the defense methods against them. We also provide insights into the future development of AIoT-oriented collaborative intelligence in the aspect of equipment sharing, model sharing, collaboration of privacy and security mechanisms, and collaboration of incentive mechanisms.

       

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