  Due to its advantages in resource constraint scenarios, Split Federated
Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity
and stragglers, SFL suffers from the challenges of low inference accuracy and
low efficiency. To address these issues, this paper presents a novel SFL
approach, named Sliding Split Federated Learning (S$^2$FL), which adopts an
adaptive sliding model split strategy and a data balance-based training
mechanism. By dynamically dispatching different model portions to AIoT devices
according to their computing capability, S$^2$FL can alleviate the low training
efficiency caused by stragglers. By combining features uploaded by devices with
different data distributions to generate multiple larger batches with a uniform
distribution for back-propagation, S$^2$FL can alleviate the performance
degradation caused by data heterogeneity. Experimental results demonstrate
that, compared to conventional SFL, S$^2$FL can achieve up to 16.5\% inference
accuracy improvement and 3.54X training acceleration.
