Decision tree-oriented transverse federation learning method

Abstract

The invention discloses a decision tree-oriented transverse federation learning method. The method comprises the steps that all participants search a quantile sketch of each feature in a data featureset based on a dichotomy; the participants construct a local histogram for each feature by using locally held data features according to the quantile sketch; noise meeting differential privacy is added to all the local histograms, and the noise is sent to a coordinator after being processed through a safety aggregation method; the coordinator merges the local histogram of each feature into a global histogram, and trains a root node of a first decision tree according to the histogram; the coordinator sends the node information to other participants; and all participants update the local histogram and repeat the above process for training to obtain a trained decision tree. The transverse federated learning method has the advantages of being easy and convenient to use, efficient in training and the like, data privacy can be protected, and quantitative support is provided for the data protection level.

Description

technical field

[0001] The invention relates to the technical field of federated learning, in particular to a decision tree-oriented horizontal federated learning method. Background technique

[0002] Federated learning, also known as ensemble learning, is a machine learning technique that jointly trains models on multiple distributed devices or servers that store data. Unlike traditional centralized learning, this method does not require data to be merged together, so the data exists independently.

[0003] The concept of federated learning was first proposed by Google in 2017, and now it has been greatly developed, and its application scenarios are becoming more and more extensive. According to different data division methods, it is mainly divided into horizontal federated learning and vertical federated learning. In horizontal federated learning, researchers distribute the training process of neural networks over multiple participants, iteratively aggregating locally tr...

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