  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.
