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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Virat et al. argued that there are two kinds of label-flipping strategies, namely static label flipping (SLF) and dynamic label flipping (DLF).
“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]
To generate a malicious model, the attacker can use a specific dataset or train the model on a benign dataset by projected gradient ascent.
“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]
Model updates should not deviate too far from benign updates to avoid detection.
“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]
Model poisoning attacks tend to be more effective in practice than data poisoning attacks because they can directly modify local model parameters.
“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]
The gradient ascent attack can be applied on a batch of target data samples all at the same time.
“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]
The gradient ascent attack 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.
“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]
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Self-citationAdvances 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 · see citing works
Self-citationFederated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · see citing works
Self-citationEMNIST: Extending MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J. C., & André, V. S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966217 · Cited by 1,782 · see citing works
Wild patterns: Ten years after the rise of adversarial machine learning
Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563 · see citing works
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 · see citing works
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H.-T., & Zhao, B. Y. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00031 · Cited by 1,486 · see citing works
(2017) · neural information processing systems · Cited by 1,413 · see citing works
Poisoning Attacks against Support Vector Machines
Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331 · see citing works
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194 · see citing works
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130 · see citing works
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Chen, X., Liu, C., Li, B., Lu, K., & Song, D. (2017) · arXiv · doi:10.48550/arxiv.1712.05526 · Cited by 1,039 · see citing works
Robust Aggregation for Federated Learning
Pillutla, K., Kakade, S. M., & Harchaoui, Z. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3153135 · Cited by 947 · see citing works
Data Poisoning Attacks Against Federated Learning Systems
Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927 · see citing works
LEAF: A Benchmark for Federated Settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.01097 · Cited by 894 · see citing works
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works
Learning Differentially Private Recurrent Language Models
McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849 · see citing works
Self-citationAdaptive Federated Optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840 · see citing works
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 · see citing works
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works
Estimating a Dirichlet Distribution
Minka, T. P. (2000) · Cited by 758 · see citing works
Ensemble Distillation for Robust Model Fusion in Federated LearningPreprint
Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07242 · Cited by 733 · see citing works
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Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2018) · arXiv · doi:10.48550/arxiv.1811.12470 · Cited by 703 · see citing works
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Shafahi, A., Huang, W., Najibi, M., Suciu, O., Studer, C., Dumitraş, T., & Goldstein, T. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000460348 · Cited by 626 · see citing works
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
Shejwalkar, V., & Houmansadr, A. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24498 · Cited by 615 · see citing works
Self-citationTowards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549 · see citing works
Ditto: Fair and Robust Federated Learning Through PersonalizationPreprint
Li, T., Hu, S., Beirami, A., & Smith, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04221 · Cited by 496 · see citing works
Federated Learning: Collaborative Machine Learning without Centralized Training Data
Abhishek, V. A., Binny, S., Johan, T. R., Raj, N., & Thomas, V. (2022) · International Journal of Engineering Technology and Management Sciences · doi:10.46647/ijetms.2022.v06i05.052 · Cited by 489 · see citing works
DBA: Distributed Backdoor Attacks against Federated Learning
Xie, C., Huang, K., Chen, P., & Li, B. (2020) · International Conference on Learning Representations · Cited by 468 · see citing works
Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works
Li, L., Xu, W., Chen, T., Giannakis, G. B., & Ling, Q. H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011544 · Cited by 392 · see citing works
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works
Can You Really Backdoor Federated Learning?
Sun, Z., Kairouz, P., Suresh, A. T., & McMahan, H. B. (2019) · arXiv · doi:10.48550/arxiv.1911.07963 · Cited by 348 · see citing works
Self-citation(2019) · neural information processing systems · Cited by 318 · see citing works
Data Poisoning Attacks on Federated Machine Learning
Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128646 · Cited by 310 · see citing works
The Limitations of Federated Learning in Sybil Settings
Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2020) · Recent Advances in Intrusion Detection · Cited by 265 · see citing works
Support vector machines under adversarial label contamination
Huang, X. X., Biggio, B., Nelson, B. A., Xiao, H., Eckert, C. M., & Roli, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.081 · Cited by 220 · see citing works
Salvaging Federated Learning by Local AdaptationPreprint
Yu, T., Bagdasaryan, E., & Shmatikov, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04758 · Cited by 220 · see citing works
A Taxonomy of Attacks on Federated Learning
Jere, M. S., Farnan, T., & Koushanfar, F. (2021) · IEEE Security & Privacy · doi:10.1109/msec.2020.3039941 · Cited by 217 · see citing works
DRACO: Byzantine-resilient Distributed Training via Redundant GradientsPreprint
Chen, L., Wang, H., Charles, Z., & Papailiopoulos, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09877 · Cited by 191 · see citing works
A Little Is Enough: Circumventing Defenses For Distributed LearningPreprint
Baruch, M., Baruch, G., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06156 · Cited by 184 · see citing works
Adversarial Label Flips Attack on Support Vector Machines
Xiao, H., Huang, X. X., & Claudia, E. (2012) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-098-7-870 · Cited by 180 · see citing works
Generalized Byzantine-tolerant SGDPreprint
Xie, C., Koyejo, O. O., & Gupta, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10116 · Cited by 172 · see citing works
Byzantine Stochastic Gradient DescentPreprint
Alistarh, D., Allen-Zhu, Z., & Li, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08917 · Cited by 172 · see citing works
signSGD with Majority Vote is Communication Efficient And Fault TolerantPreprint
Bernstein, J., Zhao, J., Azizzadenesheli, K., & Anandkumar, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05291 · Cited by 149 · see citing works
Generative Poisoning Attack Method Against Neural NetworksPreprint
Yang, C., Wu, Q., Li, H. H., & Chen, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01340 · Cited by 141 · see citing works
Provably Secure Federated Learning against Malicious Clients
Cao, X., Jia, J., & Gong, N. Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16849 · Cited by 141 · see citing works
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge TransferPreprint
Chang, H., Shejwalkar, V., Shokri, R., & Houmansadr, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11279 · Cited by 131 · see citing works
Self-citationCRFL: Certifiably Robust Federated Learning against Backdoor AttacksPreprint
Xie, C., Chen, M., Chen, P., & Li, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08283 · Cited by 121 · see citing works
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.10987 · Cited by 106 · see citing works
Attack-Resistant Federated Learning with Residual-based ReweightingPreprint
Fu, S., Xie, C., Li, B., & Chen, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11464 · Cited by 78 · see citing works
Mitigating Backdoor Attacks in Federated LearningPreprint
Wu, C., Xian, Y., Zhu, S., & Mitra, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01767 · Cited by 69 · see citing works
On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis
Newell, A., Potharaju, R., Xiang, L., & Nita-Rotaru, C. (2014) · doi:10.1145/2666652.2666661 · Cited by 46 · see citing works
Poisoning Attacks with Generative Adversarial NetsPreprint
Muñoz-González, L., Pfitzner, B., Russo, M., Carnerero-Cano, J., & Lupu, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07773 · Cited by 46 · see citing works
Federated Evaluation and Tuning for On-Device Personalization: System Design & ApplicationsPreprint
Paulik, M., Seigel, M., Mason, H., Telaar, D., Kluivers, J., van Dalen, R., Lau, C. W., Carlson, L., Granqvist, F., Vandevelde, C., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08503 · Cited by 37 · see citing works
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
Data, D., & Diggavi, S. N. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9518248 · Cited by 30 · see citing works
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and DefensesPreprint
Goldblum, M., Tsipras, D., Xie, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10544 · Cited by 24 · see citing works
SGD: Decentralized Byzantine Resilience.Preprint
Mhamdi, E. M. E., Guerraoui, R., Guirguis, A., & Rouault, S. (2019) · arXiv (Cornell University) · Cited by 13 · see citing works
The Hidden Vulnerability of Distributed Learning in Byzantium
Mhamdi, E. M. E., Guerraoui, R., & Rouault, S. (2018) · arXiv · doi:10.48550/arxiv.1802.07927
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Cited by
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Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works
Poisoning attacks in federated learning: A survey
Xia, G., Chen, J., Yu, C., & Ma, J. (2023) · IEEE Access · doi:10.1109/access.2023.3238823 · Cited by 212 · see citing works
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
Cao, X., & Gong, N. Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00383 · Cited by 205 · see citing works
The Impact of Adversarial Attacks on Federated Learning: A Survey
Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322785 · Cited by 191 · see citing works
Adversarial machine learning :
Vassilev, A., Oprea, A., Fordyce, A. J., & Anderson, H. S. (2024) · doi:10.6028/nist.ai.100-2e2023 · Cited by 194 · see citing works
Ferrag, M. A., Friha, O., Kantarcı, B., Tihanyi, N., Cordeiro, L. C., Debbah, M. A., Hamouda, D., Al-Hawawreh, M. S., & Choo, K. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3317242 · Cited by 170 · see citing works
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works
“1) Data Poisoning-Based Backdoor Attacks: Earlier works on data-based backdoor attacks focused on un-targeted attacks that aimed to degrade the accuracy of the global model by injecting “poisoned” data into the training set [231], [232].”
BackgroundA Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Liu, Z., Jiang, Y., Shen, J., Peng, M., Lam, K., Yuan, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3679014 · Cited by 148 · see citing works
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Li, S., Ngai, E. C. ‐., & Voigt, T. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3237397 · Cited by 126 · see citing works
ELSA: Secure Aggregation for Federated Learning with Malicious Actors
Rathee, M., Shen, C., Wagh, S., & Popa, R. A. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179468 · Cited by 117 · see citing works
RoFL: Robustness of Secure Federated Learning
Lycklama, H., Burkhalter, L., Viand, A., Küchler, N., & Hithnawi, A. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179400 · Cited by 114 · see citing works
Efficient and Secure Federated Learning Against Backdoor Attacks
Miao, Y., Xie, R., Li, X., Liu, Z., Choo, K. R., & Deng, R. H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3354736 · Cited by 94 · see citing works
Uddin, M. P., Xiang, Y., Hasan, M., Bai, J., Zhao, Y., & Gao, L. (2025) · ACM Computing Surveys · doi:10.1145/3727643 · Cited by 94 · see citing works
Byzantine-Robust Decentralized Federated Learning
Fang, M., Zhang, Z., Hairi, Khanduri, P., Liu, J., Lu, S., Liu, Y., & Gong, N. Z. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670307 · Cited by 80 · see citing works
A robust analysis of adversarial attacks on federated learning environments
Nair, A. K., Raj, E. D., & Sahoo, J. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103723 · Cited by 70 · see citing works
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Wu, C., Wu, F., Qi, T., Huang, Y., & Xie, X. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539119 · Cited by 68 · see citing works
A survey for federated learning evaluations: Goals and measures
Chai, D., Wang, L., Yang, L., Zhang, J., Chen, K., & Yang, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3382002 · Cited by 64 · see citing works
SafeFL: MPC-friendly Framework for Private and Robust Federated Learning
Gehlhar, T., Marx, F., Schneider, T. H., Suresh, A., Wehrle, T., & Yalame, H. (2023) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw59333.2023.00012 · Cited by 60 · see citing works
Dependable federated learning for IoT intrusion detection against poisoning attacks
Yang, R., He, H. X., Wang, Y., Qu, Y., & Zhang, W. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103381 · Cited by 58 · see citing works
Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis
Struppek, L., Hintersdorf, D., & Kersting, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00423 · Cited by 57 · see citing works
RoseAgg: Robust Defense Against Targeted Collusion Attacks in Federated Learning
Yang, H., Xi, W., Shen, Y., Wu, C., & Zhao, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3352415 · Cited by 48 · see citing works
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Jebreel, N., Domingo‐Ferrer, J., Blanco-Justicia, A., & Sánchez, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212627 · Cited by 46 · see citing works
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Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41 · see citing works
Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Li, Y., Guo, Z., Yang, N., Chen, H., Yuan, D., & Ding, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3563537 · Cited by 38 · see citing works
Riedel, P., Schick, L., von Schwerin, R., Reichert, M., Schaudt, D., & Hafner, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02234-z · Cited by 38 · see citing works
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
Zhang, X., Liu, Q., Ba, Z., Hong, Y., Zheng, T., Lin, F., Lü, L., & Ren, K. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3410014 · Cited by 36 · see citing works
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Xu, J., Wang, R., Koffas, S., Liang, K., & Picek, S. (2022) · Annual Computer Security Applications Conference · doi:10.1145/3564625.3567999 · Cited by 34 · see citing works
Federated learning vulnerabilities, threats and defenses: A systematic review and future directions
Almutairi, S., & Barnawi, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100947 · Cited by 35 · see citing works
Majeed, A., Zhang, X., & Hwang, S. O. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040127 · Cited by 35 · see citing works
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
Qin, Z., Yao, L., Chen, D., Li, Y., Ding, B., & Cheng, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599898 · Cited by 33 · see citing works
A survey on privacy-preserving federated learning against poisoning attacks
Feng, X., & Cheng, W. (2024) · Cluster Computing · doi:10.1007/s10586-024-04629-7 · Cited by 33 · see citing works
LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning
Buyukates, B., So, J., Mahdavifar, H., & Avestimehr, S. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3391849 · Cited by 31 · see citing works
Olszewski, D., Lu, A., Stillman, C., Warren, K., Kitroser, C., Pascual, A., Ukirde, D., Butler, K. R. B., & Traynor, P. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623130 · Cited by 33 · see citing works
PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack
Wang, J., Chang, X., Mišić, J., Mišić, V. B., & Wang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288936 · Cited by 32 · see citing works
FedDefender: Client-Side Attack-Tolerant Federated Learning
Park, S., Han, S., Wu, F., Kim, S., Zhu, B., Xie, X., & Cha, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599346 · Cited by 30 · see citing works
RobustFed: A Truth Inference Approach for Robust Federated Learning
Tahmasebian, F., Lou, J., & Xiong, L. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557439 · Cited by 26 · see citing works
Towards the Robustness of Differentially Private Federated Learning
Qi, T., Wang, H., & Huang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i18.29967 · Cited by 26 · see citing works
Split Aggregation: Lightweight Privacy-Preserving Federated Learning Resistant to Byzantine Attacks
Lu, Z. G., Lu, S., Cui, Y., Tang, X., & Wu, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3402993 · Cited by 27 · see citing works
Mao, Y., Ye, Z., Yuan, X., & Zhong, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3416042 · Cited by 25 · see citing works
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Khalil, M., Shakya, R., & Liu, Q. (2025) · International Conference on New Trends in Computing Sciences (ICTCS) · doi:10.1109/ictcs65341.2025.10989403 · Cited by 24 · see citing works
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
Wei, K., Li, J., Ding, M., Ma, C., Jeon, Y., & Poor, H. V. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3274119 · Cited by 24 · see citing works
Zheng, Y., Chang, C.-H., Huang, S.-H., Chen, P., & Picek, S. (2024) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2024.3477348 · Cited by 27 · see citing works
Distributed Learning in the IoT–Edge–Cloud Continuum
Arzovs, A., Judvaitis, J., Nesenbergs, K., & Selavo, L. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010015 · Cited by 24 · see citing works
FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning
Campos, E. M., Gonzalez-Vidal, A., Hernández-Ramos, J. L., & Skarmeta, A. (2025) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3474484 · Cited by 22 · see citing works
Wainakh, A., Zimmer, E., Subedi, S., Keim, J., Grube, T., Karuppayah, S., Guinea, A. S., & Mühlhäuser, M. (2022) · Sensors · doi:10.3390/s23010031 · Cited by 24 · see citing works
Collusive Model Poisoning Attack in Decentralized Federated Learning
TAN, S., Hao, F., Gu, T., Li, L., & Liu, M. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3342901 · Cited by 22 · see citing works
Securing federated learning: a defense strategy against targeted data poisoning attack
Khraisat, A., Alazab, A., Alazab, M., Jan, T., Singh, S., & Uddin, M. A. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00108-6 · Cited by 21 · see citing works
zPROBE: Zero Peek Robustness Checks for Federated Learning
Ghodsi, Z., Javaheripi, M., Sheybani, N., Zhang, X., Huang, K., & Koushanfar, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00448 · Cited by 21 · see citing works
Kumar, K. N., Mohan, C. K., Cenkeramaddi, L. R., & Awasthi, N. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103024 · Cited by 21 · see citing works
Artificial intelligence security and privacy: a survey
He, X., Xu, G., Han, X., Wang, Q., Zhao, L., Shen, C., Lin, C., Zhao, Z., Li, Q., Yang, L., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-025-4388-5 · Cited by 23 · see citing works
Efficient and Privacy-Preserving Federated Learning Against Poisoning Adversaries
Zhao, J., Zhu, H., Wang, F., Zheng, Y., Lu, R., & Li, H. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3377931 · Cited by 20 · see citing works
Get Rid of Your Trail : Remotely Erasing Backdoors in Federated Learning
Alam, M., Lamri, H., & Maniatakos, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3465441 · Cited by 19 · see citing works
Almutairi, S., & Barnawi, A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103295 · Cited by 19 · see citing works
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
Zhu, Y., Wu, Y., Luo, Z., Ooi, B. C., & Xiao, X. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3665844.3665860 · Cited by 24 · see citing works
ANODYNE: Mitigating backdoor attacks in federated learning
Gu, Z., Shi, J., & Yang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125359 · Cited by 18 · see citing works
MODEL: A Model Poisoning Defense Framework for Federated Learning via Truth Discovery
Min-zhe, W., Zhao, B., Xiao, Y., Deng, C., Liu, Y., & Liu, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3461449 · Cited by 18 · see citing works
Evaluating Security and Robustness for Split Federated Learning Against Poisoning Attacks
Wu, X., Yuan, H., Li, X., Ni, J., & Lu, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3490861 · Cited by 17 · see citing works
Byzantines Can Also Learn From History: Fall of Centered Clipping in Federated Learning
Özfatura, K., Özfatura, E., Küpçü, A., & Gündüz, D. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3345171 · Cited by 17 · see citing works
Benchmarking robustness and privacy-preserving methods in federated learning
Alebouyeh, Z., & Bidgoly, A. J. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.01.009 · Cited by 17 · see citing works
Manzoor, H. U., Hussain, S., Flynn, D., & Zoha, A. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114871 · Cited by 17 · see citing works
Siren+: Robust Federated Learning With Proactive Alarming and Differential Privacy
Guo, H., Wang, H., Song, T., Yang, H., Ma, R., Jin, X., Xue, Z., & Guan, H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3362534 · Cited by 17 · see citing works
Zhang, Z., Wu, L., He, D., Li, J., Lu, N., & Wei, X. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3379440 · Cited by 16 · see citing works
Security Analysis of SplitFed Learning
Khan, M. A., Shejwalkar, V., Houmansadr, A., & Anwar, F. M. (2022) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3560905.3568302 · Cited by 15 · see citing works
Self-citationWang, X., Wu, L., & Guan, Z. (2023) · Information Sciences · doi:10.1016/j.ins.2023.120068 · Cited by 15 · see citing works
SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks
Liu, Z., He, W., Chang, C.-H., Ye, J., Li, H., & Li, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3420135 · Cited by 15 · see citing works
Scope: On Detecting Constrained Backdoor Attacks in Federated Learning
Huang, S., Li, Y., Yan, X., Gao, Y., Chen, C., Shi, L., Chen, B., & Ng, W. W. Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3533899 · Cited by 15 · see citing works
Zhang, C., Zhang, X., Yang, X., Liu, B., Zhang, Y., & Zhou, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103131 · Cited by 16 · see citing works
Shahid, A. R., Imteaj, A., Badsha, S., & Hossain, M. Z. (2023) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp58114.2023.00085 · Cited by 14 · see citing works
FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system
Li, X., Wang, N., Yuan, S., & Guan, Z. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103936 · Cited by 14 · see citing works
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Li, S., Ngai, E. C. ‐., Ye, F., Ju, L., Zhang, T., & Voigt, T. (2024) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi61053.2024.00018 · Cited by 16 · see citing works
Security threats to agricultural artificial intelligence: Position and perspective
Gao, Y., Camtepe, S. A., Sultan, N. H., Bui, H. T., Mahboubi, A., Aboutorab, H., Bewong, M., Islam, R., Islam, M. Z., Chauhan, A., et al. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109557 · Cited by 17 · see citing works
Federated Learning Minimal Model Replacement Attack Using Optimal Transport: An Attacker Perspective
Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3516555 · Cited by 14 · see citing works
Robust and Verifiable Privacy Federated Learning
Lu, Z. G., Lu, S., Tang, X., & Wu, J. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3309273 · Cited by 13 · see citing works
LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network
Sameera, K. M., Vinod, P., A., R. R. K., & Conti, M. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110768 · Cited by 13 · see citing works
Have You Poisoned My Data? Defending Neural Networks Against Data Poisoning
Gaspari, F. D., Hitaj, D., & Mancini, L. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_5 · Cited by 13 · see citing works
RobustPFL: Robust Personalized Federated Learning
Chen, G., Wang, W., Wu, Y., Li, C., Xu, G., Ji, S., Li, T., Shen, M., & Han, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3526840 · Cited by 13 · see citing works
Dual-domain based backdoor attack against federated learning
Li, G., Chang, R., Wang, Y., & Wang, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129424 · Cited by 13 · see citing works
Toward a Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
Qiu, H., Ma, H., Zhang, Z., Abuadbba, A., Kang, W., Fu, A., & Gao, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324318 · Cited by 13 · see citing works
Backdoor Attacks in Peer-to-Peer Federated Learning
Syros, G., Yar, G., Boboila, S., Nita-Rotaru, C., & Oprea, A. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3691633 · Cited by 13 · see citing works
Manzoor, H. U., Arshad, K., Assaleh, K. T., & Zoha, A. (2024) · doi:10.36227/techrxiv.171468119.91309905/v1 · Cited by 13 · see citing works
On the Pitfalls of Security Evaluation of Robust Federated Learning
Khan, M. A., Shejwalkar, V., Houmansadr, A., & Anwar, F. M. (2023) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw59333.2023.00011 · Cited by 12 · see citing works
Self-citationSecure and Lightweight Feature Selection for Horizontal Federated Learning
Liu, X., Li, H., Xu, G., Zhang, X., Zhang, T., & Zhou, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3422850 · Cited by 12 · see citing works
A Proactive Defense Against Model Poisoning Attacks in Federated Learning
Yan, H., Zheng, C., Chen, Q., Li, X., Wang, B., Li, H., & Lin, X. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3533029 · Cited by 12 · see citing works
Ma, J., Zhou, Y., Li, Q. L., Sheng, Q. Z., Cui, L., & Liu, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3572527 · Cited by 12 · see citing works
Luong, T. D., Tien, V. M., Quyen, N. H., Hien, D. T. T., Duy, P. T., & Pham, V.-H. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103916 · Cited by 11 · see citing works
An Optimal Two-Step Approach for Defense Against Poisoning Attacks in Federated Learning
Ali, Y., Han, K., Majeed, A., Lim, J. S., & Hwang, S. O. (2025) · IEEE Access · doi:10.1109/access.2025.3556906 · Cited by 11 · see citing works
Cyber Threat Intelligence and Security for Federated Learning in Digital Forensics
Iyengar, S. S., Nabavirazavi, S., Hariprasad, Y., Prasad, H. B., & Mohan, C. K. (2025) · Signals and communication technology · doi:10.1007/978-3-031-89327-8_6 · Cited by 11 · see citing works
Collusion-Based Poisoning Attacks Against Blockchained Federated Learning
Zhang, X., Shen, T., Bai, F., & Zhang, C. (2023) · IEEE Network · doi:10.1109/mnet.2023.3318179 · Cited by 10 · see citing works
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Shejwalkar, V., Lyu, L., & Houmansadr, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00436 · Cited by 10 · see citing works
Self-citationFederated Learning: Challenges, SoTA, Performance Improvements and Application Domains
Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10 · see citing works
Hintersdorf, D., Struppek, L., & Kersting, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73741-1_16 · Cited by 10 · see citing works
Samplable Anonymous Aggregation for Private Federated Data Analysis
Talwar, K., Wang, S., McMillan, A., Feldman, V., Bansal, P., Basile, B., Cahill, Á., Chan, Y. S., Chatzidakis, M., Chen, J., et al. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690224 · Cited by 10 · see citing works
ScionFL: Efficient and Robust Secure Quantized Aggregation
Ben-Itzhak, Y., Möllering, H., Benny Pinkas, B., Schneider, T. H., Suresh, A., Tkachenko, O., Vargaftik, S., Weinert, C., Yalame, H., & Yanai, A. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00031 · Cited by 10 · see citing works
FedSV: Byzantine-Robust Federated Learning via Shapley Value
Otmani, K., El-Azouzi, R., & Labatut, V. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622175 · Cited by 9 · see citing works
FinBack: Infiltrating Backdoors into Gradient Compressors on Federated Learning
Tang, X., Wen, Y., Peng, L., Shen, M., Zhang, T., Weng, Y., Kang, J., & Niyato, D. T. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3633157 · Cited by 9 · see citing works
Exploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things
Li, K., Yuan, X., Zheng, J., Ni, W., & Guizani, M. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc58020.2023.10183217 · Cited by 8 · see citing works
- 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 › Artificial Intelligence › Security and Verification in Computing
- Keywords
- federated learning, poisoning attacks, untargeted attacks, threat model, adversary capabilities, data poisoning, model poisoning, defense mechanisms, production systems
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