The invention relates to the field of decentration computing, and provides a fair and efficient multi-center federated
consensus method based on a Shapley value, which cancels a central
server parameter aggregation stage, enables nodes to be continuously trained on local data, takes each node as a central
server and executes aggregation operation on each node. The objective of the invention is to solve the problem that decentration fairness is lack and global / local model performance is difficult to consider in the prior art. According to the main scheme, after each round of training is finished, the Shapley values of all nodes participating in
consensus are calculated to quantify the marginal contribution of each node to a
global model; cancelling a parameter aggregation mechanism of the central
server, enabling each node to independently execute local parameter aggregation operation, and dynamically and randomly selecting a target node from all nodes based on a verifiable random function; and carrying out reweighting
processing on the nodes participating in aggregation based on the Shapley
value set, and carrying out fusion updating on the nodes and
model parameters of the nodes to obtain locally optimized
model parameters.