Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.

翻译：第六代（6G）网络预期将智能地支持广泛的智慧服务和创新应用。这一背景要求大量运用机器学习技术，尤其是深度学习，以促进创新并简化能够满足所设想的6G服务多样化需求的智能网络功能/操作的部署。具体而言，协作式机器学习/深度学习涉及部署一组分布式智能体，它们在不共享数据的情况下协作训练学习模型，从而改善数据隐私并降低时间/通信开销。本文全面研究了如何在6G无线网络上有效部署协作学习。特别地，我们的研究聚焦于分裂联邦学习，这是一种近期出现的、相比现有协作学习方法具有更优性能潜力的技术。我们首先概述三种新兴的协作学习范式，即联邦学习、分裂学习和分裂联邦学习，同时阐述6G网络及其主要愿景与关键发展时间线。随后，我们强调了分裂联邦学习对即将到来的6G网络的各方面需求，涵盖6G技术（例如智能物理层、智能边缘计算、零接触网络管理、智能资源管理）和6G应用场景（例如智能电网2.0、工业5.0、互联与自主系统）。此外，我们回顾了有助于在6G网络中实施分裂联邦学习的现有数据集与框架。最后，我们识别了与分裂联邦学习使能6G网络相关的关键技术挑战、开放问题及未来研究方向。