A Decision Tree-Oriented Horizontal Federated Learning Approach
A learning method and decision tree technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low efficiency and long running time, and achieve easy use, ensure safety, and reduce the time required for running. Effect
Active Publication Date: 2022-07-22
ZHEJIANG UNIV
View PDF0 Cites 0 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
[0005] The purpose of the present invention is to provide a decision tree-oriented horizontal federated learning method, which solves the problems of low efficiency and long running time in the process of horizontal federated learning
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment
[0049] Using the data of four hospitals A, B, C, and D, a model is jointly trained by the federated learning method of the present invention to calculate the probability of a patient suffering from a certain disease. Due to the limited number of patients in a single hospital and limited training data, it is feasible to use data from multiple hospitals to train the model simultaneously. Each of the four hospitals holds data (X A , y A ), (X B , y B ), (X C , y C ), (X D , y D ),in for training data, for its corresponding label, The training data for the four hospitals contains different samples, but with the same characteristics. Due to patient privacy considerations or other reasons, each hospital cannot share data with any other hospital, so the data is stored locally. To address this situation, the four hospitals can jointly train a model using the decision tree-oriented horizontal federated learning approach shown below:
[0050] Step S101, based on the data ...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The invention discloses a decision tree-oriented horizontal federated learning method. The method includes: all participants search for a quantile sketch of each feature in a data feature set based on a binary method; There are data features, and a local histogram is constructed for each feature; noise that satisfies differential privacy is added to all local histograms, and is processed by a secure aggregation method and sent to the coordinator; the coordinator merges the local histograms of each feature is a global histogram, and trains the root node of the first decision tree according to the histogram; the coordinator sends the node information to the remaining participants; all participants update the local histogram and repeat the above process for Train to get the trained decision tree. The horizontal federated learning method of the present invention has the advantages of simple use, efficient training, etc., can protect data privacy, and provide quantitative support for 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 decentralized devices or servers that store data. Unlike traditional centralized learning, this method does not need to merge data 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 the application scenarios are becoming more and more extensive. According to the different ways of data division, it is mainly divided into horizontal federated learning and vertical federated learning. In horizontal federated learning, researchers distribute the training process of a neural network across multiple participants, iteratively aggregating locally ...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24323
Inventor 田志华张睿侯潇扬刘健任奎
Owner ZHEJIANG UNIV