Improved Prim block chain network transmission optimization method combining training loss and privacy loss

The invention relates to an improved Prim block chain network transmission optimization method combining training loss and privacy loss, and belongs to the technical field of block chains and privacy computing. The method comprises the following steps: firstly, adding Laplacian noise into a to-be-trained data set, then carrying out local training, and obtaining training loss Lf according to a training result; and calculating privacy loss Lp according to a noise mechanism added into the data set. Secondly, calculating a comprehensive loss value according to Li = lambda Lf + eta Lp; and finally, constructing a minimum spanning tree by using a Prim algorithm according to the comprehensive loss evaluation value of the node, and selecting the node on the branch with the minimum loss as a consensus node. According to the invention, the communication between the nodes in the block chain network can be optimized, the transmission expandability of the block chain network is improved, and the contradiction between data