The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we (i) provide an overview of TinyML and LargeML, (ii) analyze the motivations and requirements for unifying these paradigms within the 6G context, (iii) examine efficient bidirectional integration approaches, (iv) review state-of-the-art solutions and their applicability to emerging 6G services, and (v) identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

翻译：从第五代（5G）向第六代（6G）网络的演进正推动着对先进机器学习（ML）解决方案的空前需求。深度学习已在移动网络与通信系统中展现出显著影响，赋能了智能医疗、智能电网、自动驾驶车辆、空中平台、数字孪生以及元宇宙等智能服务。与此同时，资源受限的物联网（IoT）设备的快速普及加速了微型机器学习（TinyML）的采用，以实现高效的端侧智能；而大型机器学习（LargeML）模型仍需大量计算资源以支持大规模物联网服务及ML生成内容。这些趋势凸显了未来6G系统中需要一种融合TinyML与LargeML的统一框架，以实现无缝连接、可扩展智能与高效资源管理。本综述全面回顾了在下一代无线网络中实现TinyML与LargeML融合的最新进展。具体而言，我们（i）概述了TinyML与LargeML的基本概念，（ii）分析了在6G背景下统一这两种范式的动机与需求，（iii）探讨了高效的双向融合方法，（iv）综述了前沿解决方案及其在6G新兴服务中的适用性，以及（v）指出了与性能优化、部署可行性、资源编排及安全性相关的关键挑战。最后，我们展望了有前景的研究方向，以指导TinyML与LargeML在智能、可扩展且高能效的6G及未来网络中的整体融合。