Vertical federated learning method, apparatus, system and device, and storage medium

A vertical federated learning method, comprising:

• determining a third sample set based on a local second sample set, and calculating a mask matrix corresponding to the third sample set;
• sending the mask matrix corresponding to the third sample set to a first data party in a vertical federated learning system, wherein the first data party is used to determine a noise-added residual vector corresponding to the third sample set based on the mask matrix, a first sample set stored in the first data party comprises a training sample with a tag, and the training sample with a tag in the first sample set has a corresponding relationship with a training sample in the second sample set;
• acquiring the noise-added residual vector from the first data party, and calculating a gradient vector based on the noise-added residual vector;
• and
• updating a local model parameter based on the gradient vector to obtain an updated model parameter.

A vertical federated learning system, comprising a first data party and at least one second data party, wherein a training sample with a tag in a first sample set of the first data party has a corresponding relationship with a training sample in a second sample set of the second data party;

• the second data party is used to determine a third sample set based on the second sample set, calculate a mask matrix corresponding to the third sample set, and send the mask matrix corresponding to the third sample set to the first data party;
• the first data party is used to calculate a noise matrix corresponding to the third sample set based on the mask matrix corresponding to the third sample set, determine a residual vector corresponding to the third sample set, determine a product of the residual vector and the noise matrix corresponding to the third sample set as a noise-added residual vector corresponding to the third sample set, and send the noise-added residual vector corresponding to the third sample set to the second data party, wherein the noise matrix is composed of a quantity of noise corresponding to each training sample in the third sample set, the quantity of noise is used for noise addition processing, the residual vector comprises a difference value between a tag value and a current predicted value of a training sample in the third sample set;
• and
• the second data party is further used to calculate a gradient vector based on the noise-added residual vector, and to update a local model parameter based on the gradient vector to obtain an updated model parameter corresponding to the second data party.