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Federated learning income distribution method and system

A distribution method and distribution system technology, applied in the field of income distribution mechanism in the federated learning process, can solve problems that cannot be directly applied in the federated learning field, and achieve the effect of process modularization and easy income and expenditure adjustment

Pending Publication Date: 2020-03-24
SHENZHEN LUOJIHUI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this income distribution mechanism cannot be directly applied to the field of federated learning

Method used

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  • Federated learning income distribution method and system
  • Federated learning income distribution method and system
  • Federated learning income distribution method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] combine Figure 1-12 Shown: a federated learning income distribution method, the steps include:

[0061] Step 1: Assess the validity of the data contributed by each data provider in the federation;

[0062] Step 2: Measure the type parameters of federated learning participants;

[0063] Step 3: Calculate the pricing of the federated learning model;

[0064] Step 4: calculating the payment for each data provider based on one or more of the validity of the data contributed by each data provider, the type parameters of the federated learning participants, and the pricing of the federated learning model;

[0065] Optional step 5: Federated learning participants report their type parameters to the federated system;

[0066] Optional Step 6: Select an optimization target;

[0067] Optional Step 7: Calculate the dynamic financing rate of the federated learning system.

[0068] The federated learning participants include data providers and model users.

[0069] The data v...

Embodiment 2

[0091] combine figure 2 As shown, Step 1: Measure the validity of the data contributed by each data provider in the federation; the validity of a set of data is a measure of the set of data, and training with data with high data validity can result in a more efficient High-quality federated learning models. Data validity is obtained by measuring one or more of data size, data quality, number of features possessed, specificity of features possessed, and contribution to model quality. The size of the data is measured by the total number of records, which is the total number of all samples. The larger the data size, the higher the quality of the trained model, and the higher the data validity score given by the system. Data quality is measured by accuracy, that is, the ratio of the number of abnormal data records to the total number of records. The higher the ratio, the higher the impact of wrong data, and the lower the data validity score given by the system. Abnormal data i...

Embodiment 3

[0162] Corresponding to the second step of embodiment, see Figure 12 As shown, the system of the present invention includes the following modules.

[0163] Module 1: data validity measurement module, used to realize step 1.

[0164]Sub-module 1.1: a communication module, used to realize sub-step 1.1. In some implementations, the communication module supports the data provider to download the data validity measurement tool provided by the federated learning system; in other implementations, the communication module supports the data provider to encrypt and upload the data to the server installed with the measurement data validity tool.

[0165] Sub-module 1.2: data validity correlation coefficient measurement module, used to realize sub-step 1.2. In some implementations, the data validity correlation coefficient measurement module provides information on one or more of data quality, data size, number of features owned, specificity of features owned, contribution to model qua...

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Abstract

The invention discloses a federated learning income distribution method and system. The method comprises the following steps: evaluating the validity of the data contributed by each data provider inthe federation; measuring type parameters of federated learning participants; calculating pricing of the federated learning model, calculating payment for each data provider based on one or more of model income, data validity parameters and type parameters of the data providers, enabling participants to report the type parameters of the participants to the federated system, selecting an optimization target, and calculating the financing interest rate of the federated learning system. The invention aims to calculate how to effectively, reasonably and fairly allocate income generated by a federated learning model to each participant, so that federated learning is digitized, recordable and easy to adjust in practical application, and income and expenditure adjustment or product updating is facilitated.

Description

technical field [0001] The present invention relates to federated learning in computer science, in particular to a method and system for implementing an income distribution mechanism in the federated learning process. Background technique [0002] In recent years, artificial intelligence applications that rely on data to drive have been in trouble. Although artificial intelligence algorithms are developing rapidly, training artificial intelligence models usually requires a large amount of data, which is often distributed in the hands of different organizations, resulting in data islands due to strict supervision to protect data privacy and security. Therefore, the Federated Machine Learning system came into being. On the premise of satisfying data security and user privacy protection, federated learning establishes a multi-party shared machine learning model through parameter exchange under the encryption mechanism, so that the machine learning model can be jointly establis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06N20/00
CPCG06N20/00G06Q30/0273
Inventor 丛明舒黄艺茁
Owner SHENZHEN LUOJIHUI TECH CO LTD
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