The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
Open accessAbstract
While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not fully understood. In this work, we aim to develop a comprehensive systemization for poisoning attacks on FL by enumerating all possible threat models, variations of poisoning, and adversary capabilities. We specifically put our focus on un-targeted poisoning attacks, as we argue that they are significantly relevant to production FL deployments. We present a critical analysis of untargeted poisoning attacks under practical, production FL environments by carefully characterizing the set of realistic threat models and adversarial capabilities. Our findings are rather surprising: contrary to the established belief, we show that FL is highly robust in practice even when using simple, low-cost defenses. We go even further and propose novel, state-of-the-art data and model poisoning attacks, and show via an extensive set of experiments across three benchmark datasets how (in)effective poisoning attacks are in the presence of simple defense mechanisms. We aim to correct previous misconceptions and offer concrete guidelines to conduct more accurate (and more realistic) research on this topic.
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In the citing paper
“Virat et al. [23] argued that there are two kinds of label-flipping strategies, namely static label flipping (SLF) and dynamic label flipping (DLF).”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [23]
In the citing paper
“To generate a malicious model w k+1 0 , the attacker can use a specific dataset (e.g., different kinds of green cars labeled as birds [29]) or train the model in a benign dataset by projected gradient ascent (while the benign clients use stochastic gradient descent) algorithm [23], [67], [77].”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [23]
In the citing paper
“To avoid detection, Virat et al. [23] also state that model updates should not deviate too far from benign updates to avoid detection.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [23]
In the citing paper
“However, model poisoning attacks tend to work better in practice because they can directly modify local model parameters [11], [23], [33], [40].”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [23]
In the citing paper
“This attack can be applied on a batch of target data samples all at the same time [73].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [73]
In the citing paper
“One such attack is the gradient ascent attack [73], where the attacker runs gradient ascent on a target data sample and observes whether its increased loss can be drastically reduced in the next communication round, if so, the sample is very likely to be in the training set.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [73]
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Metadata
- Authors
- Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D.
- Affiliations
- University of Massachusetts Amherst (US)
- Google (United States) (US)
- Venue
- 2022 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2022-05
- Pages
- 1354-1371
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W4288057793
- Cited by
- 292
- References
- 64
- Citations by year
- 2026: 53 · 2025: 78 · 2024: 97 · 2023: 52 · 2022: 12
- FWCI
- 32.95
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1553301, 1953786, 1739462)
- U.S. Department of Defense
- Defense Advanced Research Projects Agency
- Naval Information Warfare Center Pacific
- 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 › Hardware and Architecture › Security and Verification in Computing
- Keywords
- federated learning, poisoning attacks, untargeted attacks, threat model, adversary capabilities, data poisoning, model poisoning, defense mechanisms, production systems
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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