Device-cloud collaborative intelligent computing,an emergent result of the development in big data,cloud computing,and edge computing,offers significant improvements in data utilization while protecting user privacy.This approach synergizes the real-time response capabilities of intelligent computing with service robustness.The study explores the application value of this computing paradigm,highlighting technical challenges such as optimizing on-device learning efficiency,mitigating overfitting with limited samples at the device,customizing on-device models,learning false associations under distributional discrepancies,and balancing communication overhead with computational efficiency.We systematically review the progress in mainstream methods within device-cloud collaborative intelligent computing,encompassing efficient computation hardware as the application cornerstone,device-centric collaborative computing,cloud-centric collaborative computing,bidirectional device-cloud collaborative computing,and trustworthy device-cloud collaborative computing.The study also summarizes applications in vertical domains such as recommendation systems,autonomous driving,security systems,and educational models.Looking toward the future of device-cloud collaborative intelligent computing,it underscores the need for focused research on cloud resource application strategies in device model personalization,multi-objective optimization algorithms for device-cloud collaboration,and optimized collaborative strategies between devices and the cloud.
device-cloud collaborationlarge and small model collaboration computingon-device computingtrustworthy collaborationmachine learning
国家科技期刊平台
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