Blockchain-Based Crowdsourcing Makes Training Dataset of Machine Learning No Longer Be in Short Supply

Abstract

Recently, machine learning has become popular in various fields like healthcare, smart transportation, network, and big data. However, the labelled training dataset, which is one of the most core of machine learning, cannot meet the requirements of quantity, quality, and diversity due to the limitation of data sources. Crowdsourcing systems based on mobile computing seem to address the bottlenecks faced by machine learning due to their unique advantages; i.e., crowdsourcing can make professional and nonprofessional participate in the collection and annotation process, which can greatly improve the quantity of the training dataset. Additionally, distributed blockchain technology can be embedded into crowdsourcing systems to make it transparent, secure, traceable, and decentralized. Moreover, truth discovery algorithm can improve the accuracy of annotation. Reasonable incentive mechanism will attract many workers to provide plenty of dataset. In this paper, we review studies applying mobile crowdsourcing to training dataset collection and annotation. In addition, after reviewing researches on blockchain or incentive mechanism, we propose a new possible combination of machine learning and crowdsourcing systems.