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
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Protecting federated learning from malicious attacks using consensus technique
Kim, W., Park, J., Cho, J.-H., Kim, D. S., Moore, T. J., Nelson, F. F., Yoon, S., & Lim, H. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114299
A Practical and Secure Byzantine Robust Aggregator
Lee, D. Z., Kolluri, A., Saxena, P., & Chang, E. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744851 · Cited by 2
Research on the Application of Blockchain Technology in Trusted Federated Learning
Zhang, D., Zhang, Q., & Miao, Z. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-93570-1_6
Secure Hierarchical Asynchronous Federated Learning with Shuffle Model and Mask–DP
Chen, Y., Ai, D., & Yan, L. (2026) · Sensors · doi:10.3390/s26020617
Inversion, Membership, and Poisoning Attacks in Federated Learning - Challenges and Defenses
Farooqi, S. A., Rahman, A. A., & Saad, A. B. (2026) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom69009.2026.11360927
RAID: A Reputation-Based Aggregation for Intrusion Detection in Federated Learning
Parvizi, N., Saha, S., & Shahriar, N. (2026) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc65079.2026.11366587
A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning
Sun, W. J., Gao, B., Xiong, K., Wang, Y., Fan, P., & Letaief, K. B. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3665336 · Cited by 2
Śmietanka, M., Liew, D., Hand, S., Loh, H. H., & Chen, Y.-Y. M. (2026) · British Actuarial Journal · doi:10.1017/s1357321725100329
Privacy-Preserving and Trustworthy AI Infrastructures for Digital Commerce
Idris, A. Z. B., Tan, W., & Rajendran, K. (2026) · Journal of Global Trends in Social Science · doi:10.70731/1178nq90
Li, K., Yuan, X., & Ni, W. (2026) · Springer series in reliability engineering · doi:10.1007/978-3-032-23959-4_6
Hegde, G., & Bhat, R. (2026) · Cogent Engineering · doi:10.1080/23311916.2026.2678666
FSBA: Feature-Space Backdoor Attacks in Federated Learning
Yin, Y., Zhang, Q., Fan, M., Wang, H., Liu, T., & Li, D. (2026) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp69961.2026.11540595
Resilience of federated learning: perspectives on attacks and defenses
Raj, P. V. P., Gupta, A., & Das, S. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344433-3.00016-2
Mazouzi, M., Jerbi, W., Cheikhrouhou, O., & Mosbah, M. (2026) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc69287.2026.11579957
Tuama, A. Y., Hasan, T. M., & Shakir, A. C. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104608
A MacroPrototype Backdoor Defense Method in Federated Learning SystemsPreprint
Chuang, I.-H., Tsai, J.-S., Lo, Y.-F., Horng, M., & Kuo, Y.-H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7428969
A Comprehensive Review on Federated and Incremental Learning in Spatial Crowdsourcing Systems
Rahman, M. M., Mamun, Q., Bewong, M., & Islam, M. Z. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70128
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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