期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 10, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3298981
关键词
Federated learning; GDPR; transfer learning
资金
- Russian Science Foundation [20-71-00034, 20-79-00194, 18-13-00479, 20-15-00398] Funding Source: Russian Science Foundation
- Korea Evaluation Institute of Industrial Technology (KEIT) [10076675] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2011-0031569, 2011-0021810, 2017R1A5A2015541, 2012M3A9C4048796, 21A20131212408, 2014R1A2A1A11050606, 22A20130012375] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1717950, 1639837] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1750162] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1545995] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1323767, 1420667] Funding Source: National Science Foundation
Today's artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.
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