Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Open accessAbstract
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates.Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks.This paper investigates whether and to what extent one can use differential Privacy (DP) to protect both privacy and robustness in FL.To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness.Our experiments show that both DP variants do defend against backdoor attacks, albeit with varying levels of protectionutility trade-offs, but anyway more effectively than other robustness defenses.DP also mitigates white-box membership inference attacks in FL, and our work is the first to show it empirically.Neither LDP nor CDP, however, defend against property inference.Overall, our work provides a comprehensive, re-usable measurement methodology to quantify the trade-offs between robustness/privacy and utility in differentially private FL.We run experiments on three datasets for backdoor attacks:
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Both LDP and CDP are effective against poisoning attacks, and CDP is more effective than LDP against poisoning attacks.
“The experimental results show that both LDP and CDP are effective against poisoning attacks, and CDP is more effective than LDP against poisoning attacks.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [66]
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Federated Learning for Tabular Data: Exploring Potential Risk to Privacy
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On the Pitfalls of Security Evaluation of Robust Federated Learning
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- Authors
- Naseri, M., Hayes, J., & De Cristofaro, E.
- Affiliations
- The Alan Turing Institute (GB)
- ORCID
- Hayes, Jamie
- Venue
- Proceedings 2022 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2022
- OpenAlex
- W3209752568
- Cited by
- 188
- References
- 134
- Citations by year
- 2026: 20 · 2025: 49 · 2024: 67 · 2023: 34 · 2022: 15 · 2021: 3
- FWCI
- 22.79
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Network and Distributed System Security Symposium (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council
- 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 › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, differential privacy, local differential privacy, central differential privacy, backdoor attacks, membership inference attacks, property inference attacks, privacy-preserving machine learning
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.
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