The development of artificial intelligence (AI) provides opportunities for
the promotion of deep neural network (DNN)-based applications. However, the
large amount of parameters and computational complexity of DNN makes it
difficult to deploy it on edge devices which are resource-constrained. An
efficient method to address this challenge is model partition/splitting, in
which DNN is divided into two parts which are deployed on device and server
respectively for co-training or co-inference. In this paper, we consider a
split federated learning (SFL) framework that combines the parallel model
training mechanism of federated learning (FL) and the model splitting structure
of split learning (SL). We consider a practical scenario of heterogeneous
devices with individual split points of DNN. We formulate a joint problem of
split point selection and bandwidth allocation to minimize the system latency.
By using alternating optimization, we decompose the problem into two
sub-problems and solve them optimally. Experiment results demonstrate the
superiority of our work in latency reduction and accuracy improvement.

本文介绍了一种模型分区 / 切割的方法，将深度神经网络切分为两个部分，分别在设备和服务器上进行联合训练和联合推理，旨在通过交替优化解决划分点选择和带宽分配问题，以最小化系统的延迟