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Security issues of federated learning in real-life applications
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Zheng, H. P., Sthapit, Saurav, Epiphaniou, Gregory and Maple, Carsten (2021) Security issues of federated learning in real-life applications. In: Competitive Advantage in the Digital Economy (CADE 2021), Online, 2-3 Jun 2021 pp. 118-124. ISBN 9781839536045. doi:10.1049/icp.2021.2409
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Official URL: http://dx.doi.org/10.1049/icp.2021.2409
Abstract
Machine Learning (ML) is becoming one of the most popular and widely used IT technologies in the past 10 years. The sharing and analysing of large volumes of data promises to revolutionalise many sectors, such as transport, healthcare and defence. This data's value and the consequent competitive advantages from its processing have attracted significant adversarial efforts against its security, privacy and availability. Recent advancements in federated learning (FL) show promising results in protecting data security and privacy and equally create additional opportunities for organised cyber criminals to capitalise from its use. This paper presents the existing and emerging security threats against FL using real-life scenarios and applications.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Publisher: | IET | ||||
ISBN: | 9781839536045 | ||||
Book Title: | Competitive Advantage in the Digital Economy (CADE 2021) | ||||
Official Date: | 15 December 2021 | ||||
Dates: |
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Page Range: | pp. 118-124 | ||||
DOI: | 10.1049/icp.2021.2409 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | Competitive Advantage in the Digital Economy (CADE 2021) | ||||
Type of Event: | Conference | ||||
Location of Event: | Online | ||||
Date(s) of Event: | 2-3 Jun 2021 |
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