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.

由于资源约束场景的优势，分割联邦学习（SFL）在 AIoT 系统中具有潜力。为了解决数据异构性和滞后问题，本文提出了一种名为滑动分割联邦学习（S^2FL）的新型 SFL 方法，该方法采用自适应滑动模型拆分策略和基于数据平衡的训练机制。实验结果表明，与传统的 SFL 相比，S^2FL 的推断准确性提高了 16.5％，训练加速了 3.54 倍。