Method, apparatus, electronic device, and readable storage medium for vertical federated learning

A method for vertical federated learning, comprising:

• upon training a random forest model based on a common sample participating in vertical federated learning, receiving, for a to-be-trained node of a decision tree of a plurality of decision trees included in the random forest model, a first ciphertext sent by a further participant in the vertical federated learning, the first ciphertext being obtained by performing semi-homomorphic encryption on a first identity indicating whether the sample is present and a category label of the sample by the further participant;
• calculating to obtain, in a ciphertext space and based on the first ciphertext, a ciphertext calculation result matching with a type of the to-be-trained node, and performing differential privacy processing based on the ciphertext calculation result to obtain a second ciphertext, and sending the second ciphertext to the further participant to enable the further participant to perform random forest model training based on the second ciphertext;
• receiving feedback information sent by the further participant, and configuring a category of the to-be-trained node or configuring a corresponding split feature value based on the feedback information and the second ciphertext;
• and
• updating the to-be-trained node, until a training finish condition is met, to obtain a target decision tree, wherein trainings for each of the plurality of decision trees in the random forest model are completed to obtain a target random forest model.

An electronic device, comprising:

• a memory and a processor;
• the memory being configured to store computer program instructions;
• the processor being configured to execute the computer program instructions to enable the electronic device to implement acts comprising:
• upon training a random forest model based on a common sample participating in vertical federated learning, performing, for a to-be-trained node of a decision tree of a plurality of decision trees included in the random forest model, semi-homomorphic encryption on a first identity indicating whether the sample is present and a category label of the sample to obtain a first ciphertext, and sending the first ciphertext to a further participant in the vertical federated learning to enable the further participant to calculate to obtain, in a ciphertext space and based on the first ciphertext, a ciphertext calculation result matching with a type of the to-be-trained node;
• receiving a second ciphertext sent by the further participant, and training the to-be-trained node based on the type of the to-be-trained node and the second ciphertext to configure a category of the to-be-trained node or configure a corresponding split feature value, the second ciphertext comprising a result obtained by performing differential privacy processing on the ciphertext calculation result by the further participant;
• and
• updating the to-be-trained node, until a training finish condition is met, to obtain a target decision tree, wherein trainings for each of the plurality of decision trees in the random forest model are completed to obtain a target random forest model.