With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable interest in recent years. However, the deployment of federated learning faces substantial challenges as massive resource-limited IoT devices can hardly support on-device model training. This leads to the emergence of split learning (SL) which enables servers to handle the major training workload while still enhancing data privacy. In this article, we offer a brief overview of key advancements in SL and articulate its seamless integration with wireless edge networks. We begin by illustrating the tailored 6G architecture to support edge SL. Then, we examine the critical design issues for edge SL, including innovative resource-efficient learning frameworks and resource management strategies under a single edge server. Additionally, we expand the scope to multi-edge scenarios, exploring multi-edge collaboration and mobility management from a networking perspective. Finally, we discuss open problems for edge SL, including convergence analysis, asynchronous SL and U-shaped SL.

翻译：随着分布式边缘计算资源的普及，6G移动网络将演变为连接智能的网络。沿此方向，将联邦学习融入移动边缘的提议近年来引起了广泛关注。然而，海量资源受限的物联网设备难以支持本地模型训练，使得联邦学习的部署面临重大挑战。这催生了分裂学习的出现——该技术使服务器能够承担主要训练负载，同时增强数据隐私保护。本文简要概述了SL的关键进展，并阐述了其与无线边缘网络的无缝集成。我们首先阐述了支持边缘SL的定制化6G架构，随后分析了单边缘服务器场景下边缘SL的关键设计问题，包括创新的资源高效学习框架与资源管理策略。此外，我们将范围扩展至多边缘场景，从网络视角探索多边缘协作与移动性管理。最后，我们探讨了边缘SL的开放性问题，包括收敛性分析、异步SL和U形SL。