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.

翻译：由于在资源受限场景中的优势，分裂联邦学习（SFL）在AIoT系统中具有广阔前景。然而，受数据异构性和滞后节点的影响，SFL面临推理精度低与效率低下的挑战。针对这些问题，本文提出一种名为滑动分裂联邦学习（S$^2$FL）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。通过根据AIoT设备的计算能力动态分配不同模型部分，S$^2$FL可缓解滞后节点导致的训练效率低下问题；通过将具有不同数据分布的设备上传的特征进行组合，生成多个更大且分布均匀的批次用于反向传播，S$^2$FL可缓解数据异构性导致的性能退化。实验结果表明，与传统SFL相比，S$^2$FL可实现高达16.5%的推理精度提升和3.54倍的训练加速。