Block chain cross-chain-based federated learning method and device

Abstract

The invention discloses a block chain cross-chain-based federated learning method and device, and the method comprises the steps: carrying out the training of a federated learning model through local data in a single block chain network, and transmitting model parameters to a model aggregation smart contract through a private transaction, thereby achieving the aggregation of different node model parameters in a single chain, synchronizing model parameters at each node; the model aggregation smart contract sending the latest model parameters to a cross-chain network through cross-chain private transaction to realize model synchronization between different chains. In the whole process, data of a single network node and data of a cross-chain network node are not exchanged, it is ensured that model parameters of each node are not leaked through a private transaction mode, training of different node data of different block chain networks on a federated learning model is achieved while data privacy security is ensured, a trained data set is expanded, and the accuracy of the model is improved. An integral mechanism is adopted to improve the enthusiasm of each member to contribute to a data training model, and the model training effect is further improved.

Description

technical field [0001] The present invention relates to the technical field of block chains, in particular to a federated learning method and equipment based on block chain cross-chain. Background technique [0002] Federated learning enables participating institutions to collaboratively train machine learning models without directly exchanging raw data. For those companies or institutions with insufficient data, this can allow them to join forces to obtain better models without exposing the original data, so as to achieve mutual benefit and win-win results. In the existing engineering technology, the collaborative training of various institutions relies on the centralized third-party collaborative nodes to realize control, aggregation and key management. Existing centralized methods have the following disadvantages: [0003] 1. Collaboration nodes will continuously obtain information uploaded by all other institutions. And a curious collaborative node can use this inform...

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Problems solved by technology

[0004] 2. When the cooperative node fails, the entire system will crash and cannot continue to operate

[0006] Although the current blockchain-based federated learning method solves some privacy issues and single-point failure problems, federated learning requires a large amount of data to achieve accurate predictions. For large-scale data, the existing blockchain-based processing capabilities Usually insufficient, and cannot completely break down barriers for different data sets

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