Semantic communication and edge-cloud collaborative intelligence are increasingly recognized as foundational enablers for next-generation intelligent services operating under stringent bandwidth, latency, and resource constraints. By shifting the communication objective from bit-perfect delivery toward the transmission of task-relevant semantic representations, semantic communication enables adaptive tradeoffs among communication overhead, inference accuracy, computational load, and end-to-end latency. This survey provides a comprehensive and system-level synthesis of recent advances in semantic communication at the edge-cloud interface, encompassing architectural models for collaborative intelligence, representation learning and semantic abstraction techniques, network-aware and resource-adaptive semantic encoding strategies, and learning-driven optimization and orchestration mechanisms. Beyond efficiency considerations, the survey situates semantic communication within practical operational contexts, including security, trust, resilience, and scalability, drawing connections to zero-trust networking, physical-layer security, and emerging edge-cloud control paradigms. Finally, open challenges and research directions are identified, highlighting the role of semantic communication as a key building block for AI-native networking and 6G-ready intelligent systems.

翻译：语义通信与边云协同智能日益被认为是下一代智能服务在严格带宽、时延和资源约束下运行的基础使能技术。通过将通信目标从比特级精确传输转向任务相关语义表征的传递，语义通信能够在通信开销、推理精度、计算负载和端到端时延之间实现自适应权衡。本综述对边云接口处语义通信的最新进展进行了全面且系统级的综合，涵盖了协同智能的架构模型、表征学习与语义抽象技术、网络感知与资源自适应的语义编码策略，以及学习驱动的优化与编排机制。除效率考量外，本综述还将语义通信置于实际运营背景中，包括安全性、可信性、韧性与可扩展性，并与零信任网络、物理层安全及新兴的边云控制范式建立联系。最后，本文指出了开放的挑战与研究方向，强调了语义通信作为AI原生网络与面向6G的智能系统关键构建模块的作用。