Design method of security edge federated learning system based on block chain and optimization algorithm thereof

Abstract

The invention provides a design method of a security edge federated learning system based on a block chain, and the method employs a consensus mechanism based on a practical Byzantine fault-tolerant method and a robust global model aggregation algorithm to resist the attack of a malicious edge device and a malicious edge server on federated learning training. The safe and credible edge federated learning system is realized, and the convergence performance of edge federated learning model training is effectively improved. In order to further improve the training speed of the block chain-based secure edge federated learning system and reduce the time delay of system model training and consensus, the invention also provides a resource optimization algorithm based on deep reinforcement learning, so that the rapid and efficient allocation of wireless bandwidth and sending power in a dynamic wireless channel environment is realized, and the resource optimization efficiency is improved. The resource utilization maximization of the secure edge federated learning system is improved, and then the training time delay minimization of the block chain-based secure edge federated learning system is realized.

Description

technical field [0001] The invention belongs to the field of intelligent Internet of things, and relates to a design and optimization method of a secure wireless federated learning system based on blockchain. Background technique [0002] In recent years, with the continuous development of cloud computing, Internet of Things and edge computing technologies, artificial intelligence has been widely used in various emerging applications, such as smart finance, smart medical care, smart transportation, etc. However, in most application scenarios, user data is usually widely distributed across various edge devices, rather than being managed centrally by a single subject. In the traditional centralized machine learning paradigm, it is required to transmit massive data distributed at the edge to the central server to train the machine learning model, which leads to serious communication bottlenecks. In addition, due to factors such as economic benefits and privacy legal policies, ...

AI Technical Summary

Problems solved by technology

Malicious edge servers mislead the training of the entire federated learning model by

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