- 中国精品科技期刊
- CCF推荐A类中文期刊
- 计算领域高质量科技期刊T1类
| Citation: |
Luo Yuzhe, Li Ling, Hou Pengpeng, Yu Jiageng, Cheng Limin, Zhang Changyou, Wu Yanjun, Zhao Chen. Survey of AIoT-Oriented Collaborative Intelligence[J]. Journal of Computer Research and Development, 2025, 62(1): 179-206. DOI: 10.7544/issn1000-1239.202330975
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Luo Yuzhe: born in 1995. PhD candidate. His main research interest includes distributed machine learning
Li Ling: born in 1982. PhD, professor.Senior member of CCF. Her mian research interest includes intelligent computing
Hou Pengpeng: born in 1985. PhD, associate professor. His main research interest includes operating system
Yu Jiageng: born in 1983. PhD, associate professor. His main research interests include operating system, cloud computing and intelligent software
Cheng Limin: born in 1988. PhD candidate. Her main research interests include intelligent system software, machine learning and video analysis
Zhang Changyou: born in 1970. PhD, professor. His main research interests include parallel distributed software and software engineering
Wu Yanjun: born in 1979. PhD, professor. Senior member of CCF. His main research interests include operating system and system security
Zhao Chen: born in 1967. PhD, professor. His main research interests include logic and automatic program analysis, and design and implementation of programming languages
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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