Towards a Unified Framework for Large–Small Model Collaboration in Cloud–Edge Systems

Foundation models have improved the reasoning and generation ability of artificial intelligence systems. However, they are difficult to deploy in edge environments with limited computation, memory, and data access. Small models are easier to run on edge devices. They support fast and low-latency inference, but they often lack global semantic reasoning and cross-domain generalization. This gap between model ability and deployment cost motivates large–small model collaboration in cloud–edge systems. This survey provides a systematic review and a knowledge-floworiented taxonomy of such collaboration. It focuses on how cloud-side large models and edge-side small models share, update, and coordinate knowledge. We review knowledge distillation, split inference, federated and continual adaptation, and elastic offloading. We also cover lightweight deployment, modular expert design, privacyaware coordination, and agent-driven orchestration. Unlike surveys on edge intelligence, federated learning, model compression, TinyML, or cloud–edge resource scheduling, this survey centers on model collaboration. We treat large–small collaboration as a knowledge-centered problem linked to real deployment constraints. We further discuss trade-offs in accuracy, latency, bandwidth, privacy, energy efficiency, adaptability, and lifecycle management. Finally, we identify open challenges for trustworthy, sustainable, and self-evolving cloud–edge collaborative intelligence.