文章基本信息

标题：Survey on Federated Learning Towards Privacy Preserving AI
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作者：Sheela Raju Kurupathi ; Wolfgang Maass
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2020
卷号：10
期号：11
页码：235-253
DOI：10.5121/csit.2020.101120
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：One of the significant challenges of Artificial Intelligence (AI) and Machine learning models is to preserve data privacy and to ensure data security. Addressing this problem lead to the application of Federated Learning (FL) mechanism towards preserving data privacy. Preserving user privacy in the European Union (EU) has to abide by the General Data Protection Regulation (GDPR). Therefore, exploring the machine learning models for preserving data privacy has to take into consideration of GDPR. In this paper, we present in detail understanding of Federated Machine Learning, various federated architectures along with different privacy-preserving mechanisms. The main goal of this survey work is to highlight the existing privacy techniques and also propose applications of Federated Learning in Industries. Finally, we also depict how Federated Learning is an emerging area of future research that would bring a new era in AI and Machine learning.
关键词：Federated Learning ;Artificial Intelligence ;Machine Learning ;Privacy ;Security ;Distributed Learning