A Blockchain-Based Machine Learning Framework for Edge Services in IIoT

Edge services provide an effective and superior means of real-time transmissions and rapid processing of information in the Industrial Internet of Things (IIoT). However, the continuous increase in the number of smart devices results in privacy leakage and insufficient model accuracy of edge services. To tackle these challenges, in this research, we propose a blockchain-based machine learning framework for edge services (BML-ES) in IIoT. Specifically, we construct novel smart contracts to encourage multi-party participation of edge services to improve the efficiency of data processing. Moreover, we propose an aggregation strategy to verify and aggregate model parameters to ensure the accuracy of the decision tree learning model. Finally, based on SM2 public-key cryptosystem, we protect data security and prevent data privacy leakage in edge services. Theoretical analysis and simulation experiments indicate that BML-ES framework is effective and efficient, and is better suitable to improve the accuracy of edge services in IIoT.