综述边缘云协同计算中智能与模型优化的融合路径。
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
- 系统梳理边缘云协同架构与关键使能技术。
- 提出压缩、自适应与神经网络搜索等模型优化方法。
- 适合关注AI部署与分布式系统优化的研究者参考。
边缘云协同计算（ECCC）已成为应对现代智能应用计算需求的关键范式，通过整合云端资源与边缘设备，实现高效低延迟处理。近年来，深度学习与大语言模型（LLMs）的发展显著提升了分布式系统的性能，但也带来了模型部署与资源管理的重大挑战。本文全面综述了分布式智能与模型优化在边缘云环境中的交汇，提供基础架构、使能技术及新兴应用的系统性教程。系统分析了模型优化方法，包括压缩、自适应与神经架构搜索，以及兼顾性能、能效与延迟的AI驱动资源管理策略。进一步探讨了隐私保护与安全增强机制，并通过自动驾驶、医疗健康与工业自动化等应用展示实际部署。性能分析与基准测试方法亦被深入研究，以建立评估标准。最后，识别出大语言模型部署、6G集成、类脑计算与量子计算等关键研究方向，为异构性管理、实时处理与可扩展性等持续挑战提供路线图。本综述连接理论进展与实践落地，为研究人员与从业者提供优化分布式计算环境的全景视角，推动下一代智能系统的创新。
原文摘要 · Abstract (English)
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.
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