  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.
