The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
A Taxonomy of Attacks on Federated Learning
Abstract
Federated learning is a privacy-by-design framework that enables training deep neural networks from decentralized sources of data, but it is fraught with innumerable attack surfaces. We provide a taxonomy of recent attacks on federated learning systems and detail the need for more robust threat modeling in federated learning environments.
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“Therefore, an adversary monitoring the communication channels can efficiently infer the private data of the participants [24].”
From Vulnerabilities in federated learning (2021) · cites this paper as [24]
Citations
Cites
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
Self-citationThe Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961
MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02249 · Cited by 2,523
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001
The security of machine learning
Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839
Research works printed but not linked: 1
- 10.1145/2810103.2813677 · DOI · Paper page
Cited by
Sorted by citations
Membership Inference Attacks on Machine Learning: A Survey
Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 662
The Roadmap to 6G Security and Privacy
Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3078081 · Cited by 440
Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2022) · ACM Computing Surveys · doi:10.1145/3560816 · Cited by 347
Metadata
- Authors
- Jere, M. S., Farnan, T., & Koushanfar, F.
- Affiliations
- University of California San Diego (US)
- Venue
- IEEE Security & Privacy
- Published
- 2021-03
- Volume
- 19
- Issue
- 2
- Pages
- 20-28
- ISSN
- 1540-7993 (ISSN-L)
- 1558-4046
- OpenAlex
- W3114953370
- Cited by
- 217
- References
- 22
- Citations by year
- 2026: 24 · 2025: 42 · 2024: 60 · 2023: 47 · 2022: 31 · 2021: 12
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Security & Privacy (publishedVersion)
- Funders
- Semiconductor Research Corporation (AUTO 2899.001)
- 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, attack taxonomy, threat modeling, privacy, deep neural network
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