Backdoor attacks-resilient aggregation based on Robust Filtering of Outliers in federated learning for image classification
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Zhang's design scheme has better performance in terms of communication and computational cost compared to Nuria's robust filtering operator, but Nuria focuses more on detection of outlier behaviors of adversarial participants.
“Compared to the robust filtering operator for one-dimensional outliers proposed by Nuria.Zhang's design scheme has better performance in terms of communication and computational cost, but Nuria focuses more on the detection of outlier behaviors of adversarial participants, with a focus on defending against model-poisoning attacks based on data poisoning and”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Rodríguez-Barroso et al. 2022b
Model-poisoning backdoor attacks take one step more than data-poisoning backdoor attacks.
“As a matter of fact, model-poisoning backdoor attacks take one step more than data-poisoning backdoor attacks [37].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [37]
The defender at the post-aggregation stage can handle model poisoning backdoor attacks, including model replacement backdoor attack and weight scaling backdoor attack.
“The defender at this stage can well handle the model poisoning backdoor attacks, including model replacement backdoor attack [37], [208], [223] and weight scaling backdoor attack [38], [194].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [37]
The attacker must obtain sufficient knowledge of the whole WFL network for the global model replacement backdoor attack, introducing a considerable attack cost.
“However, the attacker must obtain sufficient knowledge of the whole WFL network, introducing a considerable attack cost, as discussed in Section III-C.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [37]
The global model replacement backdoor attack launched during the aggregation process achieves the highest BAR while maintaining a reasonable MAR.
“It can be seen that the global model replacement backdoor attack [37] launched during the aggregation process achieves the highest BAR while also maintaining a reasonable MAR.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [37]
The global model replacement proposed in [37] is conducted when the global model is about to converge.
“Specifically, the global model replacement proposed in [37] is conducted when the global model is about to converge.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [37]
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- Authors
- Rodríguez-Barroso, N., Martínez-Cámara, E., Luzón, M. V., & Herrera, F.
- Affiliations
- Universidad de Granada (ES)
- Corresponding author
- Rodríguez-Barroso, Nuria
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 245
- Pages
- 108588
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4220822151
- Cited by
- 38
- References
- 44
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 12 · 2023: 5 · 2022: 3
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
- Funders
- Agencia Estatal de Investigacion (PID2020-119478GB-I00, PID2020-116118GA-I00, EQC2018-005084-P, IJC2018-036092-I)
- Gobierno de Espana Ministerio de Ciencia e Innovacion
- ERDF (EQC2018-005084-P, PID2020-119478GB-I00)
- European Social Fund (IJC2018-036092-I)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- backdoor attacks, model poisoning, federated learning, robust aggregation, outlier detection, image classification, adversarial defense
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