  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.
