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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| Published in | Computer (Long Beach, Calif.) Vol. 57; no. 4; pp. 46 - 56 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9162 1558-0814 |
| DOI | 10.1109/MC.2023.3324975 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Huang, Ren-Yi Chang, J. Morris Samaraweera, Dumindu |
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| Cites_doi | 10.1109/tdsc.2021.3128679 10.1109/tifs.2022.3169918 10.1109/tifs.2022.3227761 10.1145/3564625.3567973 10.1145/3133956.3133982 10.1109/CVPR52688.2022.00988 10.1145/3338501.3357370 10.1109/jsac.2023.3242702 10.1109/spw53761.2021.00017 10.1145/3338501.3357371 10.1016/j.ins.2024.120527 10.1002/int.22818 10.1109/CVPRW56347.2022.00383 10.1109/tcss.2023.3296885 10.1016/j.array.2022.100207 |
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| References | ref13 ref12 Buyukates (ref14) 2022 ref15 ref20 ref11 Zhang (ref1) 2022 ref10 ref2 ref16 ref19 ref18 ref8 Zhang (ref5) 2022 ref9 Jiang (ref17) 2021 ref4 ref3 Panda (ref7) 2022 ref6 |
| References_xml | – ident: ref4 doi: 10.1109/tdsc.2021.3128679 – year: 2021 ident: ref17 article-title: FLASHE: Additively symmetric homomorphic encryption for cross-silo federated learning – start-page: 7587 volume-title: Proc. Int. Conf. Artif. Intell. Statist. year: 2022 ident: ref7 article-title: SparseFed: Mitigating model poisoning attacks in federated learning with sparsification – ident: ref8 doi: 10.1109/tifs.2022.3169918 – ident: ref3 doi: 10.1109/tifs.2022.3227761 – ident: ref16 doi: 10.1145/3564625.3567973 – ident: ref11 doi: 10.1145/3133956.3133982 – ident: ref15 doi: 10.1109/CVPR52688.2022.00988 – volume-title: Proc. Workshop Federated Learn., Recent Adv. New Challenges (Conjunction NeurIPS) year: 2022 ident: ref14 article-title: LightVeriFL: Lightweight and verifiable secure federated learning – ident: ref19 doi: 10.1145/3338501.3357370 – start-page: 26,429 volume-title: Proc. Int. Conf. Mach. Learn. year: 2022 ident: ref1 article-title: Neurotoxin: Durable backdoors in federated learning – ident: ref13 doi: 10.1109/jsac.2023.3242702 – ident: ref12 doi: 10.1109/spw53761.2021.00017 – ident: ref20 doi: 10.1145/3338501.3357371 – ident: ref9 doi: 10.1016/j.ins.2024.120527 – ident: ref18 doi: 10.1002/int.22818 – ident: ref2 doi: 10.1109/CVPRW56347.2022.00383 – ident: ref6 doi: 10.1109/tcss.2023.3296885 – year: 2022 ident: ref5 article-title: Flip: A provable defense framework for backdoor mitigation in federated learning – ident: ref10 doi: 10.1016/j.array.2022.100207 |
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| Snippet | This article presents a comprehensive survey of both attack and defense mechanisms within the federated learning (FL) landscape. Furthermore, it explores the... |
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| SubjectTerms | Federated learning Robustness (mathematics) Surveys |
| Title | Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey |
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