Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164531
Title: Privacy and robustness in federated learning: attacks and defenses
Authors: Lyu, Lingjuan
Yu, Han
Ma, Xingjun
Chen, Chen
Sun, Lichao
Zhao, Jun
Yang, Qiang
Yu, Philip S.
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q. & Yu, P. S. (2022). Privacy and robustness in federated learning: attacks and defenses. IEEE Transactions On Neural Networks and Learning Systems, PP, 1-21. https://dx.doi.org/10.1109/TNNLS.2022.3216981
Project: NWJ-2020-008
NSC-2019-011
AISG2-RP-2020-019 
A20G8b0102 
NTU NAP 
FCPNTU-RG-2021-014
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continues to thrive in this new reality. Existing FL protocol designs have been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this article, we conduct a comprehensive survey on privacy and robustness in FL over the past five years. Through a concise introduction to the concept of FL and a unique taxonomy covering: 1) threat models; 2) privacy attacks and defenses; and 3) poisoning attacks and defenses, we provide an accessible review of this important topic. We highlight the intuitions, key techniques, and fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions toward robust and privacy-preserving FL, and their interplays with the multidisciplinary goals of FL.
URI: https://hdl.handle.net/10356/164531
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2022.3216981
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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