The invention discloses a model optimization method and
system based on quantitative splitting
federated learning, and relates to the technical field of
customer edge computing, and the method comprises the following steps: S1, constructing an initial
global model which comprises a
server model and a
client model; s2, adaptively selecting a split layer according to the resources of the
client, and determining the time
delay and
energy consumption of the split layer under the initial
global model; s3, constructing a
global model optimization problem based on the initial global model; s4, obtaining a corresponding
client transmission power strategy, a client quantification strategy and a client
bandwidth allocation rate strategy; and S5, training the initial global model by using the customer data sample to obtain a trained global model. Under the condition that the model
training time delay is reduced,
energy consumption is reduced, client computing resources are saved, the aggregation precision of the global model is improved, and optimization of the global model is achieved.