  To alleviate the shortage of computing power faced by clients in training
deep neural networks (DNNs) using federated learning (FL), we leverage the edge
computing and split learning to propose a model-splitting allowed FL (SFL)
framework, with the aim to minimize the training latency without loss of test
accuracy. Under the synchronized global update setting, the latency to complete
a round of global training is determined by the maximum latency for the clients
to complete a local training session. Therefore, the training latency
minimization problem (TLMP) is modelled as a minimizing-maximum problem. To
solve this mixed integer nonlinear programming problem, we first propose a
regression method to fit the quantitative-relationship between the cut-layer
and other parameters of an AI-model, and thus, transform the TLMP into a
continuous problem. Considering that the two subproblems involved in the TLMP,
namely, the cut-layer selection problem for the clients and the computing
resource allocation problem for the parameter-server are relative independence,
an alternate-optimization-based algorithm with polynomial time complexity is
developed to obtain a high-quality solution to the TLMP. Extensive experiments
are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and
the results verify the validity and improved performance of the proposed SFL
framework.
