Evolved PoW: Integrating the Matrix Computation in Machine Learning Into Blockchain Mining

【Author】 Wei, Yunkai; An, Zixian; Leng, Supeng; Yang, Kun

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT). However, the model training in machine learning demands tremendous computing resource, bringing heavy burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote large amount of computing resource to compete for generating valid blocks, which is frequently disputed for tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW) consensus that can integrate the matrix computations in machine learning into the process of blockchain mining. The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively, designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously salvage the computing power of the miners for the model training in machine learning. We conduct experiments to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to 80% computing power from pure blockchain mining for parallel model training in machine learning.

【Keywords】Task analysis; Blockchains; Machine learning; Training; Complexity theory; Internet of Things; Computational modeling; Blockchain; consensus; evolved Proof of Work (E-PoW); machine learning; matrix computation

【发表时间】2023 15-Apr

【收录时间】2023-05-26

【文献类型】理论模型

【主题类别】

区块链技术-核心技术-挖矿策略