大数据2025，Vol.11Issue(3)：62-77,16.DOI:10.11959/j.issn.2096-0271.2025036
分布式模型缓存赋能端云协同自演进视频分析
Distributed model cache-driven device-cloud collaboration in adaptive video analytics
摘要
Abstract
In video analysis,data drift is one of the main issues affecting system inference performance.To address this,this paper proposes a mobile-oriented edge-cloud collaborative continuous learning framework.Specifically,this framework deploys a model caching on resource-constrained mobile devices and designs an edge-cloud collaborative hierarchical adaptation strategy that integrates"reuse,fine-tuning,and retraining".Constructing a prefix tree storage structure based on domain attributes enables rapid model retrieval,thereby reducing cloud queuing delays during adaptations.Evaluation results demonstrate that this framework outperforms existing state-of-the-art solutions in object detection and image classification tasks,achieving a 34.5-times reduction in reuse response time and a 1.99-times reduction in retraining time while achieving optimal overall accuracy in multi-device system adaptations.This highlights the framework's significant potential for enhancing response speed and system scalability.关键词
端云协同/视频分析/边缘计算/持续学习Key words
device-cloud collaboration/video analytics/edge computing/continuous learning分类
计算机与自动化引用本文复制引用
廖天俊,赵茆哲,刘生钟,吴帆..分布式模型缓存赋能端云协同自演进视频分析[J].大数据,2025,11(3):62-77,16.基金项目
国家重点研发计划项目(No.2022ZD0119100) （No.2022ZD0119100）
国家自然科学基金项目(No.62472278,No.62025204,No.62432007,No.62441236,No.62332014,No.62332013) The National Key Research and Development Program of China(No.2022ZD0119100),The National Natural Science Foundation of China(No.62472278,No.62025204,No.62432007,No.62441236,No.62332014,No.62332013) （No.62472278,No.62025204,No.62432007,No.62441236,No.62332014,No.62332013）
