A method and device for determining the contribution degree of a participant

Abstract

The invention discloses a method and device for determining the contribution degree of a participant, which is suitable for a horizontal federated model obtained by N participants, where N is greater than 1, wherein the method includes: according to the model accuracy and the local model of each participant The preset model determines the expected contribution index of the local model of each participant; determines the expected contribution index of the horizontal federation model according to the model accuracy of the horizontal federated model and the preset model; and determines the expected contribution index of the local model of the N participants and the expected contribution index of the horizontal federated model to determine the degree of contribution of at least one participant to the horizontal federated model. The technical solution is used to reduce the processing pressure of the system, improve the performance of the system, and further improve the processing efficiency of the system for determining the contribution degree.

Description

technical field [0001] Embodiments of the present invention relate to the field of financial technology (Fintech), and in particular to a method and device for determining the contribution of a participant. Background technique [0002] With the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into financial technology, and machine learning technology is no exception. However, due to the security and real-time requirements of the financial and payment industries, it is also Higher requirements for machine learning technology. [0003] Federated learning refers to the method of machine learning by combining different participants. Horizontal federated learning in federated learning is to take out the part of the data whose user data features are the same but the users are not completely the same for joint machine learning when the data characteristics of each partic...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, the horizontal federated learning system determines the contribution of each participant to the horizontal federated learning according to the amount of local data of each participant, but the local data of each participant may have data quality problems, and the local data of each participant The format and form are different, and the quality optimization processing methods are different. The system needs to perform quality optimization processing on the data of each participant, but this quality optimization processing method will consume a lot of system resources, increase the processing pressure of the system, and reduce the system performance. The processing efficiency of the system to determine the contribution degree is low

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