survey

Federated Machine Learning: Concept and Applications

Authors:

Yongxin TongAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 2

Article No.: 12, Pages 1 - 19

https://doi.org/10.1145/3298981

Published: 28 January 2019 Publication History

Abstract

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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Index Terms

Federated Machine Learning: Concept and Applications
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Security and privacy

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 2

Survey Papers and Regular Papers

March 2019

214 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3306498

Editor:
Yu Zheng
JD Finance, China

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Publication History

Published: 28 January 2019

Accepted: 01 November 2018

Received: 01 September 2018

Published in TIST Volume 10, Issue 2

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