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.

提出了一种新的 SFL 框架 ——MergeSFL，通过结合特征合并和批量大小调节来解决统计异质性和系统异质性带来的挑战，从而提高 SFL 的性能。在 80 个 NVIDIA Jetson 边缘设备上进行了广泛实验，实验结果显示，与基准结果相比，MergeSFL 可以提高最终模型的准确性 5.82% 至 26.22%，并加速 1.74 倍至 4.14 倍。