Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd AL system designed to address these challenges. CrowdAL integrates blockchain for transparency and a tamper-proof incentive mechanism, using smart contracts to evaluate crowd workers' performance and aggregate labeling results, and employs zero-knowledge proofs to protect worker privacy.
@article{arxiv.2503.00066,
title = {CrowdAL: Towards a Blockchain-empowered Active Learning System in Crowd Data Labeling},
author = {Shaojie Hou and Yuandou Wang and Zhiming Zhao},
journal= {arXiv preprint arXiv:2503.00066},
year = {2025}
}
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Published in 2024 IEEE 20th International Conference on e-Science (e-Science)