Federal learning member reasoning attack defense method and device based on block chain decentration

Abstract

The invention discloses a federated learning member reasoning attack defense method and device based on block chain decentration. The method comprises the following steps: an edge end participating in federated learning performs model training by using local data to obtain a gradient of a local model; the edge end carries out workload proving through computing power, obtains the accounting right of the account book block according to the workload proving, compresses the gradient of the local model, uploads the compressed model gradient to the account book block, and uplinks the account book block containing the compressed gradient as a block chain node; edge ends corresponding to other account book blocks are broadcast on the block chain by the account book block; and one edge end on the block chain is randomly selected as a temporary central server end in each round, the compressed model gradient of each edge end on the block chain is aggregated to obtain an aggregation model of each round, and the aggregation model is issued to the edge end for next training. The aim of defending the data member reasoning attack can be fulfilled.

Description

technical field [0001] The invention belongs to the technical field of federated learning security, and in particular relates to a method and device for defending against reasoning attacks of federated learning members based on blockchain decentralization. Background technique [0002] With the continuous development of artificial intelligence technology, people feel the convenience brought by technology, but also gradually increase the demand for privacy protection. As the commercial scope of deep learning technology becomes wider and wider, the generation of valuable data privacy serious concern. Data leakage incidents emerge one after another, and data leakage occurs during data storage, data transmission, and data sharing, resulting in serious interests and security issues for data owners and providers. [0003] Federated learning is a new type of distributed learning framework that has gradually emerged in recent years, which allows training data to be shared among mul...

AI Technical Summary

Problems solved by technology

Under the framework of federated learning, the edge end and the server end need to interact and update the gradient, so the server end must be an organization trusted by each edge end, otherwise, once the server end is leaked or the server end is obtained by a malicious attacker, then all Data privacy security at the edge poses a huge threat

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