This paper presents a novel split learning (SL) framework, referred to as SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink transmission over multiple access channels. The key strategy is to divide devices into multiple groups and allow the devices within the same group to simultaneously transmit their smashed data and device-side models over the multiple access channels. The optimization problem of device grouping to minimize SL latency is formulated, and the benefit of device grouping in reducing the uplink latency of SL is theoretically derived. By examining a two-device grouping case, two asymptotically-optimal algorithms are devised for device grouping in low and high signal-to-noise ratio (SNR) scenarios, respectively, while providing proofs of their optimality. By merging these algorithms, a near-optimal device grouping algorithm is proposed to cover a wide range of SNR. Simulation results demonstrate that our SL framework with the proposed device grouping algorithm is superior to existing SL frameworks in reducing SL latency.

翻译：本文提出一种新型分裂学习（SL）框架，称为SplitMAC，通过利用多接入信道上的同步上行传输来降低SL延迟。核心策略是将设备划分为多个组，并允许同一组内的设备在多接入信道上同时传输其压缩数据和设备侧模型。本文构建了以最小化SL延迟为目标的设备分组优化问题，并从理论上推导了设备分组在降低SL上行延迟中的优势。通过分析两设备分组场景，分别针对低信噪比和高信噪比情形设计了两种渐近最优的设备分组算法，并给出了最优性证明。通过融合上述算法，提出了一种覆盖广泛信噪比范围的近最优设备分组算法。仿真结果表明，与现有SL框架相比，本文所提SL框架结合所提出的设备分组算法在降低SL延迟方面具有显著优势。