  Edge AI has been recently proposed to facilitate the training and deployment
of Deep Neural Network (DNN) models in proximity to the sources of data. To
enable the training of large models on resource-constraint edge devices and
protect data privacy, parallel split learning is becoming a practical and
popular approach. However, current parallel split learning neglects the
resource heterogeneity of edge devices, which may lead to the straggler issue.
In this paper, we propose EdgeSplit, a novel parallel split learning framework
to better accelerate distributed model training on heterogeneous and
resource-constraint edge devices. EdgeSplit enhances the efficiency of model
training on less powerful edge devices by adaptively segmenting the model into
varying depths. Our approach focuses on reducing total training time by
formulating and solving a task scheduling problem, which determines the most
efficient model partition points and bandwidth allocation for each device. We
employ a straightforward yet effective alternating algorithm for this purpose.
Comprehensive tests conducted with a range of DNN models and datasets
demonstrate that EdgeSplit not only facilitates the training of large models on
resource-restricted edge devices but also surpasses existing baselines in
performance.
