Federal learning data participant contribution value calculation and excitation method based on block chain

Abstract

The invention discloses a federal learning data participant contribution value calculation and excitation method based on a block chain, and the method comprises the steps: carrying out the feature extraction of a local training model of a data participant, and obtaining model updating data; enabling a data demander to decode the model update data to obtain an intermediate verification model; enabling the data demand side to evaluate the intermediate verification model in batches to obtain evaluation indexes; calculating the contribution degree of the data participant according to the evaluation index; distributing excitation values to the data participants according to the contribution degrees of the data participants; and forming a reward queue by the excitation values and the addresses of the data participants, and putting the reward queue on the block chain. According to the method, the contribution of the data demander to the data participant is evaluated and calculated, and then the contribution of each party is used as a distribution basis for reward distribution, so that the fairness is ensured, and meanwhile, the enthusiasm of the data owner to participate in federated learning is improved; and the data owners are effectively encouraged and attracted to participate in federal learning.

Description

technical field [0001] The invention relates to the technical field of federated learning, in particular to a blockchain-based federated learning data participant contribution value calculation and incentive method. Background technique [0002] In recent years, artificial intelligence technology has developed rapidly and has been widely used in various industries. Artificial intelligence driven by the big data environment has entered a golden period of development. Some potential problems currently exist may cause big data-driven artificial intelligence technology to enter a trough in development, which needs to be faced and resolved: First, the problem of data sources, including limited data volume and data quality problems. In many industries, data exists in the form of data islands, and there are many obstacles to the integration of data between industries and even within industries. The second is data privacy and data security issues. How to effectively integrate and ...

AI Technical Summary

Problems solved by technology

Second, data privacy and data security issues

Evaluation methods and reward mechanisms are inaccurate and unfair

At the same time, this method will include the model information in the federated learning process, the trained model of the participants, the test data, and the contribution, etc., in the blockchain. Due to the openness of the blockchain, there may be learning models and even data privacy. risk of leakage

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