A survey on federated learning in data mining,WIREs Data Mining and Knowledge Discovery

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A survey on federated learning in data mining
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-12-09 , DOI: 10.1002/widm.1443
Bin Yu ₁ , Wenjie Mao ₁ , Yihan Lv ₁ , Chen Zhang ₁ , Yu Xie ₂

Affiliation

Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is also a privacy security issue when mining data. A collaboratively decentralized approach called federated learning unites multiple participants to generate a shareable global optimal model and keeps privacy-sensitive data on local devices, which may bring great hope to us for solving the problems of decentralized data and privacy protection. Though federated learning has been widely used, few systematic studies have been conducted on the subject of federated learning in data mining. Hence, different from prior reviews in this field, we make a comprehensive summary and provide a novel taxonomy of the application of federated learning in data mining. This article starts by providing a thorough description of the relevant definitions and concepts, followed by an in-depth investigation on the challenges faced by federated learning. In this context, we elaborate four taxonomies of major applications of federated learning in data mining, including education, healthcare, IoT, and intelligent transportation, and discuss them comprehensively. Finally, we discuss four promising research directions for further research, that is, privacy enhancement, improvement of communication efficiency, heterogeneous system processing, and reducing economic costs.

中文翻译：

数据挖掘中的联邦学习调查

数据挖掘是从数据中提取未知、隐藏和潜在有用信息的过程。但是数据孤岛的问题使得人们对分散的数据进行收集和分析变得困难，并且在挖掘数据时也存在隐私安全问题。一种称为联邦学习的协作去中心化方法联合多个参与者生成可共享的全局最优模型，并将隐私敏感数据保存在本地设备上，这可能为我们解决去中心化数据和隐私保护问题带来巨大希望。尽管联邦学习已被广泛使用，但很少有关于数据挖掘中的联邦学习主题的系统研究。因此，与该领域的先前评论不同，我们对联邦学习在数据挖掘中的应用进行了全面的总结并提供了一种新颖的分类法。本文首先对相关定义和概念进行了详尽的描述，然后对联邦学习面临的挑战进行了深入调查。在此背景下，我们详细阐述了联邦学习在数据挖掘中的主要应用的四个分类，包括教育、医疗保健、物联网和智能交通，并对其进行全面讨论。最后，我们讨论了进一步研究的四个有前景的研究方向，即隐私增强、通信效率的提高、异构系统处理和降低经济成本。随后对联邦学习面临的挑战进行了深入调查。在此背景下，我们详细阐述了联邦学习在数据挖掘中的主要应用的四个分类，包括教育、医疗保健、物联网和智能交通，并对其进行全面讨论。最后，我们讨论了进一步研究的四个有前景的研究方向，即隐私增强、通信效率的提高、异构系统处理和降低经济成本。随后对联邦学习面临的挑战进行了深入调查。在此背景下，我们详细阐述了联邦学习在数据挖掘中的主要应用的四个分类，包括教育、医疗保健、物联网和智能交通，并对其进行全面讨论。最后，我们讨论了进一步研究的四个有前景的研究方向，即隐私增强、通信效率的提高、异构系统处理和降低经济成本。

更新日期：2021-12-09

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