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. Our SL framework is also extended to consider practical fading channels and to support a general group size. 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：本文提出了一种新颖的分裂学习框架，称为SplitMAC，通过利用多址接入信道上的同步上行传输来降低分裂学习的延迟。关键策略是将设备划分为多个组，并允许同一组内的设备通过多址接入信道同时传输其切割数据与设备端模型。本文构建了以最小化分裂学习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对降低分裂学习上行延迟的益处。通过分析两设备分组案例，分别针对低信噪比与高信噪比场景设计了两种渐近最优的设备分组算法，并给出了其最优性证明。通过融合这些算法，提出了一种适用于广泛信噪比范围的近最优设备分组算法。所提出的分裂学习框架还扩展至实际衰落信道场景，并支持通用分组大小。仿真结果表明，与现有分裂学习框架相比，本文所提框架结合所提设备分组算法在降低分裂学习延迟方面更具优势。