期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2020
卷号：43
期号：3
页码：26-36
出版社：IEEE Computer Society
摘要：The success of artificial intelligence (AI) is inseparable from large-scale and high-quality data, which is not always available. Involving human forces like crowdsourcing can help provide more training data and improve data quality for AI tasks. But with more privacy concerns and stricter laws, the data isolation problem is becoming worse, just when federated learning (FL) has emerged as a promising solution. In this article, we highlight the core issues in federated learning in the lens of crowdsourcing, including privacy and security, incentive mechanism, communication optimization and quality control. We expect to inspire the design of federated learning systems with existing crowdsourcing techniques. We also discuss emerging future challenges to implement a fully fledged federated learning platform.
