Federated learning method and device based on block chain

Abstract

The invention discloses a federated learning method and device based on a block chain. The method comprises the steps: determining the block chain; enabling the coordinator node to create a federatedlearning task according to the model original data sent by each participant node; receiving training data obtained by local training of the participant nodes; sending the to-be-updated parameters to other participant nodes according to the training data, so as to enable the other participant nodes to update own model parameters according to the to-be-updated parameters; and after model training iscompleted, issuing reward resources according to training data provided by each participant node in the training process, and writing rewards into the block chain. Compared with a traditional mode, the mutual trust problem of all parties is effectively solved; all parties participating in federated learning negotiate together to generate a coordinator node, so that the transparency of the processis improved; federated learning whole-process data is recorded in a block chain, so that the traceability of data operation is ensured; all parties are encouraged to actively participate through rewarding resources, and the enthusiasm of participants is improved.

Description

technical field [0001] This application relates to the field of federated learning, in particular to a blockchain-based federated learning method and device. Background technique [0002] Federated Learning (Federated Learning) is an emerging artificial intelligence basic technology. It was first proposed by Google in 2016. It was originally used to solve the problem of updating models locally for end users of Android phones. Its design goal is to guarantee big data exchange. Under the premise of information security, protection of terminal data and personal data privacy, and compliance with laws and regulations, efficient machine learning is carried out among multiple participants or computing nodes. In most industries, data exists in the form of isolated islands. Due to issues such as industry competition, privacy security, and complex administrative procedures, even the realization of data integration between different departments of the same company faces many obstacles....

AI Technical Summary

Problems solved by technology

[0003] However, in the existing technology, due to the inability to accurately evaluate the data contribution of each participant, this makes it difficult to divide the interests according to the contribution of each participant

Moreover, mutual trust cannot be reached between the participants, which will also reduce the training efficiency of the model during federated learning

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