论文标题
在制造业中应用联合学习
Application of federated learning in manufacturing
论文作者
论文摘要
在私营部门和行业中，每分钟都会创建大量数据。尽管在私人娱乐领域掌握数据通常很容易，但在工业生产环境中，由于法律，知识产权保存和其他因素，因此更加困难。但是，大多数机器学习方法都需要数量和质量方面足够的数据源。将两个要求融合在一起的一种合适方法是在整个学习进度的情况下联合学习，但每个人仍然是他们数据的所有者。 Federate学习首先是Google研究人员在2016年提出的，例如用于改进Google的键盘Gboard。与数十亿个Android用户相反，可比机械仅由少数公司使用。本文研究了哪些其他限制在生产中占上风以及可以考虑哪种联合学习方法。
A vast amount of data is created every minute, both in the private sector and industry. Whereas it is often easy to get hold of data in the private entertainment sector, in the industrial production environment it is much more difficult due to laws, preservation of intellectual property, and other factors. However, most machine learning methods require a data source that is sufficient in terms of quantity and quality. A suitable way to bring both requirements together is federated learning where learning progress is aggregated, but everyone remains the owner of their data. Federate learning was first proposed by Google researchers in 2016 and is used for example in the improvement of Google's keyboard Gboard. In contrast to billions of android users, comparable machinery is only used by few companies. This paper examines which other constraints prevail in production and which federated learning approaches can be considered as a result.
