Federal learning method and system based on block chain and trusted execution environment

Abstract

The invention relates to a federated learning method and system based on a block chain and a trusted execution environment, and belongs to the technical field of artificial intelligence machine learning. A block chain and a trusted execution environment technology are combined, and in a task collection stage, a task owner broadcasts and initiates a crowdsourcing model training task in a block chain network. After the task is received, the node meeting the requirement applies to join a participant contract, and a task publisher randomly selects participants meeting the training requirement number from all applicants and issues the task. The selected participant locally trains the model, and meanwhile, in the TEE environment of the selected participant, the correctness proof of model training is generated by comparing whether the Hash values updated by the model are consistent or not. After all the models are trained and updated, the participants send updated models and certificates to an aggregation contract for model aggregation and verification, and corresponding rewards are issued to the participating nodes after verification is passed. High-confidence verification is realized, and the problem that training participants are not credible is solved.

Description

technical field [0001] The invention relates to a federated learning method and system based on a block chain and a trusted execution environment, and belongs to the technical field of artificial intelligence machine learning. Background technique [0002] As a promising technology, machine learning has become a research hotspot in the computer field, and its theories and methods have been widely used in engineering applications and scientific fields. [0003] Federated Learning (Federated Learning) is an emerging artificial intelligence technology originally used to solve the problem of updating models locally by end users of Android phones. Its design goal is to ensure information security and protect terminal data and personal data when exchanging big data. Under the premise of ensuring privacy and ensuring legal compliance, efficient machine learning is carried out among multiple participants or computing nodes. Federated learning can effectively help multiple organizat...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to solve the technical problem of how to realize secure multi-party cooperative learning in an untr

Images