Accelerating Split Federated Learning over Wireless Communication
  Networks
Ce Xu Jinxuan Li Yuan Liu Yushi Ling Miaowen Wen
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.