From distributed machine learning to federated learning: In the view of data privacy and security-Reference-Cited by-同舟云学术

From distributed machine learning to federated learning: In the view of data privacy and security

Published:2020-09-23 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Shen Sheng¹^ORCID,Zhu Tianqing¹,Wu Di²,Wang Wei¹,Zhou Wanlei¹

Affiliation:

1. School of Computer Science, Center for Cyber Security and Privacy University of Technology Sydney Ultimo New South Wales Australia

2. School of Computer Science University of Technology Sydney Ultimo New South Wales Australia

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6002

Reference73 articles.

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2. KonečnỳJ McMahanHB YuFX RichtárikP SureshAT BaconD. Federated learning: strategies for improving communication efficiency;2016. arXiv preprint arXiv:1610.05492.

3. McMahanHB MooreE RamageD HampsonS. Communication‐efficient learning of deep networks from decentralized data;2016. arXiv preprint arXiv:1602.05629.

4. Federated machine learning: Concept and applications;Yang Q;ACM Trans Intell Syst Technol,2019

5. BonawitzK EichnerH GrieskampW et al. Towards federated learning at scale: system design;2019. arXiv preprint arXiv:1902.01046.

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