Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey

This article presents a comprehensive survey of both attack and defense mechanisms within the federated learning (FL) landscape. Furthermore, it explores the challenges involved and outlines future directions for the development of a robust and efficient FL solution.

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Bibliographic Details
Published inComputer (Long Beach, Calif.) Vol. 57; no. 4; pp. 46 - 56
Main Authors Huang, Ren-Yi, Samaraweera, Dumindu, Chang, J. Morris
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Federated learning
Robustness (mathematics)
Surveys
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Summary:This article presents a comprehensive survey of both attack and defense mechanisms within the federated learning (FL) landscape. Furthermore, it explores the challenges involved and outlines future directions for the development of a robust and efficient FL solution.
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ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2023.3324975