Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Fangcheng Fu

Yong Cheng

Yangyu Tao

Bo Li

Yuhong Liu

Jie Jiang

Chen Hou

Pin Xiao

Jiawei Jiang

Junwei Pan

Huanran Xue

Embodiments of this application provide a training method, apparatus, and device for a federated neural network model, a computer program product, and a computer-readable storage medium. The method includes: performing forward computation processing of sample data through a first model, to obtain an output vector of the first model; performing forward computation processing through the interaction layer of the output vector of the first bottom model, at last one model parameter of an interaction layer, and at least one encrypted model to obtain an output vector of the interaction layer; performing forward computation processing on the output vector of the interaction layer through the second model, to obtain an output vector of the federated neural network model; back propagating the federated neural network model according to a loss result corresponding to the output vector of the federated neural network model, and updating the at least one model parameter of the interaction layer, a parameter of the first bottom model, and a parameter of the top model according to a back propagation processing result; and obtaining a trained federated neural network model based on the updated model parameters of the interaction layer, the first bottom model, and the second model.

Training method, apparatus, and device for federated neural network model, computer program product, and computer-readable storage medium

This application discloses a method to train a decision tree model. The method is performed by a training system. The training system includes N processing subnodes and a main processing node, N being a positive integer greater than 1. The method includes separately obtaining, by each processing subnode for a currently being trained tree node, a node training feature set and gradient data of the currently being trained tree node; separately determining, by each of the processing subnode, a local splitting rule for the currently being trained tree node according to the node training feature set and the gradient data that are obtained, and transmitting the local splitting rule to the main processing node; and selecting, by the main processing node, a splitting rule corresponding to the currently being trained tree node from the local splitting rule determined by each of the processing subnode.

Training method and system for decision tree model, storage medium, and prediction method

A data processing method includes: acquiring a first intersection set, acquiring a second intersection set, calculating an intersection between the first intersection set and the second intersection set to obtain an intersection result set that includes an intersecting portion of the first intersection data and the second intersection data, and obfuscating the intersection result set to obtain an obfuscation set that includes obfuscated data based on data in the second intersection set and an intersection data set based on the intersection result set.

Data processing method and apparatus, device, and computer-readable storage medium

This disclosure relates to a method for vertical federated learning. In multiple participation nodes deployed in a multi-way tree topology, an upper-layer participation node corresponds to k lower-layer participation nodes. After the upper-layer participation node and the k lower-layer participation nodes exchange public keys with each other, the upper-layer participation node performs secure two-party joint computation with the lower-layer participation nodes with a first public key and second public keys as encryption parameters to obtain k two-party joint outputs of a federated model. Further, the upper-layer participation node aggregates the k two-party joint outputs to obtain a first joint model output corresponding to the federated model. As such, a multi-way tree topology deployment-based vertical federated learning architecture is provided, improving the equality of each participation node in a vertical federated learning process.

Method and apparatus for vertical federated learning

A secure multi-party computation method and apparatus, a device, and a storage medium are provided, which belong to the field of data security technologies. The method includes: performing homomorphic encryption processing on first plaintext data to generate first ciphertext data; transmitting encrypted data containing the first ciphertext data to a second node device; receiving difference ciphertext data transmitted by the second node device; and decrypting the difference ciphertext data to obtain a positive and negative property of a difference between the first plaintext data and the second plaintext data. The foregoing method and apparatus, device, and storage medium are conducive to reducing the number of times of communications of secure multi-party computation, have low communication overhead and high computation efficiency, and enable magnitude comparison and equality testing to be performed simultaneously.

Growing community of inventors

Shenzhen, China

Fangcheng Fu