Training Latency Minimization for Model-Splitting Allowed Federated Edge
  Learning
Yao Wen Guopeng Zhang Kezhi Wang Kun Yang
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.