Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen, Kaibin Huang
Abstract
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 SL and articulate its seamless integration with wireless edge networks. We begin by illustrating the tailored 6G architecture to support split edge learning (SEL). Then, we examine the critical design issues for SEL, including resource-efficient learning frameworks and resource management strategies under a single edge server. Furthermore, from a networking perspective, we expand the scope to multi-edge scenarios, exploring multiedge collaboration and model placement/migration. Finally, we discuss open problems for SEL, including convergence analysis, asynchronous SL, and label privacy preservation.