Marine Internet of Things data security sharing method under edge computing framework based on federated learning and block chain technology

Abstract

The invention discloses a marine Internet of Things data security sharing method under an edge computing framework based on federated learning and a block chain technology, and the method comprises the steps: firstly the parameter quality and reputation of edge nodes are calculated, and the selection of the edge nodes are carried out; secondly, the edge server issues an initial model to the selected edge node, and the edge node performs local training by using a local data set; then, the edge server updates the global model by using the local training data parameters collected from the edge nodes, trains the global model in each iteration, and updates the reputation and quality metrics; and finally, the alliance block chain is used as a decentralized method, and effective reputation and quality management of workers are achieved under the condition of no reputation and tampering. Besides, a reputation consensus mechanism is introduced into the block chain, so that edge nodes recorded in the block chain are higher in quality, and the overall model effect is improved. According to the invention, the marine Internet of Things edge computing framework has more efficient data processingand safer data protection capabilities.

Description

technical field [0001] The invention belongs to the technical field of edge computing, and in particular relates to a method for safely sharing marine Internet of Things data under an edge computing framework based on federated learning and block chain technology. Background technique [0002] In recent years, with its characteristics of anonymity, non-tampering and distribution, blockchain has provided a safe and reliable solution for edge computing between multiple untrusted parties, and has become a new research hotspot in the field of edge computing. . At the same time, federated learning technology, as an emerging technology with powerful functions in data processing in recent years, is more suitable for integration with blockchain and applied in the field of edge computing by virtue of its distributed and local computing characteristics. The specific application of blockchain combined with federated learning technology in edge computing is: using federated learning te...

AI Technical Summary

Problems solved by technology

However, this method only considers the reputation of edge nodes, and does not consider the parameter quality of nodes

[0004] To sum up, the data security sharing technology based on federated learning and blockchain technology mainly has the following defects: (1) It does not consider an efficient federated learning worker screening scheme, that is, both reputation and parameter quality are considered

(2) Does not consider the appropriate consensus mechanism for the server when adding the blockchain

(3) Existing methods may not be suitable for complex marine environments

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