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Meta-federated learning
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The defense schemes proposed in [204] and [205] require modifying the local training process, and the backdoor effect can only be suppressed and cannot be entirely eliminated.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [205]
In the citing paper
“To better defend against both data poisoning backdoor attacks and model poisoning attacks, the Meta-FL is developed in [205].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [205]
In the citing paper
“In each round, the central server selects subsets of the participants. Each subset computer updates. Then, each subset follows the SecAgg protocol and sends the cryptographymasked updates. Although the server cannot access each client's update, it can migrate the effect of backdoor attacks by ignoring visibly different subset updates.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [205]
In the citing paper
“The method requires the number of malicious attacks to be smaller than 50% of the total selected clients in each round.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [205]
In the citing paper
“In such a design, the participant's privacy is ensured, and it can be applied to defend against label-flipping-based, trigger-based, and model replacement-based backdoor attacks.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [205]
Citations
The citation lists haven’t been fetched yet
References 41 · Cited by 12
Research works printed but not linked: 1
- Weiszfeld, 1937, Sur le point pour lequel la somme des distances de n points donnés est minimum, Tohoku Mathematical Journal, First Series, 43, 355
Metadata
- Authors
- Aramoon, O., Chen, P.-Y., Qu, G., & Tian, Y.
- Affiliations
- University of Maryland, College Park (US)
- IBM (United States) (US)
- University of Virginia (US)
- Venue
- Federated Learning
- Publisher
- Elsevier
- Published
- 2024
- Pages
- 161-179
- OpenAlex
- W3127875616
- Cited by
- 12
- References
- 41
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4
- FWCI
- 5.92
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Elsevier eBooks (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- federated learning, backdoor attacks, secure aggregation, adversarial attacks, adversarial triggers, privacy-preserving machine learning, robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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