Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication burden on resource-constrained workers and protect model privacy, split federated learning (SFL) has been released by integrating both data and model parallelism. Despite resource limitations, SFL still faces two other critical challenges in EC, i.e., statistical heterogeneity and system heterogeneity. To address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. Concretely, feature merging aims to merge the features from workers into a mixed feature sequence, which is approximately equivalent to the features derived from IID data and is employed to promote model accuracy. While batch size regulation aims to assign diverse and suitable batch sizes for heterogeneous workers to improve training efficiency. Moreover, MergeSFL explores to jointly optimize these two strategies upon their coupled relationship to better enhance the performance of SFL. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson edge devices, and the experimental results show that MergeSFL can improve the final model accuracy by 5.82% to 26.22%, with a speedup by about 1.74x to 4.14x, compared to the baselines.

翻译：近年来，联邦学习已成为边缘AI在边缘计算系统中挖掘有价值知识的主流技术。为缓解资源受限工作节点的计算/通信负担并保护模型隐私，通过整合数据并行与模型并行，分裂联邦学习应运而生。尽管已解决资源限制问题，SFL在边缘计算中仍面临两个关键挑战：统计异质性与系统异质性。为此，我们提出新型SFL框架MergeSFL，通过引入特征合并与批大小调节机制。具体而言，特征合并旨在将各工作节点的特征融合为混合特征序列，该序列近似等价于IID数据导出的特征，从而提升模型精度；而批大小调节则为异质工作节点分配差异化的适配批大小以提升训练效率。此外，MergeSFL基于两者间的耦合关系探索联合优化策略，进一步增强SFL性能。在搭载80块NVIDIA Jetson边缘设备的物理平台上开展的大量实验表明，相较基线方法，MergeSFL可将最终模型精度提升5.82%至26.22%，并实现约1.74倍至4.14倍的加速比。