计算机科学
联合学习
云计算
信息隐私
数据科学
移动设备
计算机安全
互联网隐私
万维网
人工智能
操作系统
作者
Zengpeng Li,Vishal Sharma,Saraju P. Mohanty
出处
期刊:IEEE Consumer Electronics Magazine
[Institute of Electrical and Electronics Engineers]
日期:2020-05-01
卷期号:9 (3): 8-16
被引量:78
标识
DOI:10.1109/mce.2019.2959108
摘要
Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed.
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