The DOI matches its Crossref record; 47 of the 52 references it cites were found in a registry or an index.
Data Poisoning Attacks Against Federated Learning Systems
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“Even with a low proportion of malicious edge servers, data poisoning attacks can significantly decrease recall and classification accuracy, as discussed by Tolpegin et al. [139].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [139]
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108
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
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906
Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760
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
(2017) · neural information processing systems · Cited by 1,413
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
Research works printed but not linked: 10
- Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. arXiv preprint arXiv:1807.00459 (2018) · arXiv
- Fung, C., Yoon, C.J., Beschastnikh, I.: Mitigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866 (2018) · arXiv
- Hard, A., et al.: Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018) · arXiv
- Kairouz, P., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019) · arXiv
- Mhamdi, E.M.E., Guerraoui, R., Rouault, S.: The hidden vulnerability of distributed learning in byzantium. arXiv preprint arXiv:1802.07927 (2018) · arXiv
- General Data Protection Regulation: Regulation (EU) 2016/679 of the European parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46. Off. J. Eur. Union (OJ) 59(1–88), 294 (2016)
- Ryffel, T., et al.: A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017 (2018) · arXiv
- Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019) · arXiv
- Truex, S., Liu, L., Gursoy, M.E., Yu, L., Wei, W.: Towards demystifying membership inference attacks. arXiv preprint arXiv:1807.09173 (2018) · arXiv
- Yang, C., Wu, Q., Li, H., Chen, Y.: Generative poisoning attack method against neural networks. arXiv preprint arXiv:1703.01340 (2017) · arXiv
Cited by
Sorted by citations
Items 901–929 of 929
Defending Poisoning Attacks in Federated Learning Under System and Data Heterogeneity
Sun, P., Liu, T., Xu, Y., Luo, J., Chen, H., Wu, L., Chen, G., & Jin, W. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3708397
Boussoum, Y., Aissani, S., & Merghem, L. (2026) · International Conference on Connected Innovation and Technology (ICCITX) · doi:10.1109/iccitx70146.2026.11679917
Embedding-Based Anomaly Detection for Poisoned Client Filtering in Federated Learning
Dhillon, H., & Singh, B. C. (2026) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi69914.2026.11685841
Rahmati, M., & Rahmati, N. (2026) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-026-00401-6
On the Fragility of Contribution Evaluation in Federated Learning
Pejó, B., Frank, M., Varga, K., Veliczky, P., & Biczók, G. (2026) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml68715.2026.00019
Unmasking Composite Adversaries: A New Threat Model for Federated Learning
Hasan, M., Xiang, Y., Uddin, M. P., Yearwood, J. L., & Gao, L. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3736807
RetFL: a privacy-preserving and traceable framework for robust federated learning
Huang, Y., Zhou, Z., Tian, Y., Chen, C., Luo, H., & Li, T. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01301-5
Wang, L., Zhu, S., Mu, H., & Fu, X. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28460-0_30
Yadav, A. K. (2026) · Journal of Information assurance and security · doi:10.2478/ias-2026-0022
Alsaraireh, S., & Al-Fayoumi, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-39783-6_20
Zaffarullah, Z. R. A., Gudala, V. M., Shanthi, J. M., Srivastava, S., Srivastava, S., & Kumar, S. (2026) · International Conference on Secure Information Systems and Technologies (ICSIST) · doi:10.1109/icsist70215.2026.11706080
Evaluation of Label-Flipping Attacks on Federated Anomaly Detection in xG Networks
Bin-Ruba, S. A., Velloso, P. B., & Gu, P. (2026) · International Conference on Future and Intelligent Networking (FINE) · doi:10.1109/fine71157.2026.00052
Beyond Robustness: Uncovering Blind Spots in Federated Learning Defenses
Sharma, G., Chamikara, M. A. P., Chhetri, M. B., & Chen, Y. P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38702-8_11
Collaborative Federated Learning Architecture for Privacy-Preserving Legal Document Inferencing
Sistla, H. S., Shiksha, S. K., Singh, S., Hussain, H. S., Kaviyaadharshani, D., & Rakshit, K. (2026) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic70896.2026.11716864
Thiyagarajan, C., Ganaselvi, J. A., & Venkatraman, K. (2026) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa69367.2026.11708460
Poisoning Federated Knowledge Graph Embeddings Under Noisy Conditions
Cha, Y., & Xie, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-5696-9_21
Federated and Distributed Learning for Secure and Explainable Cyber Defense
Barot, V., Zamani, A. S., Modi, A., Patel, V., Dabhi, D., & Prasad, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1970-2.ch012
SemLRA: Lightweight Robust Aggregation Against Poisoning Attacks in Semantic Federated Learning
Liu, Z., Jin, Z., Li, Z., & Luo, H. (2026) · Electronics · doi:10.3390/electronics15204621
MMT: Achieving Exact Federated Unlearning with Improved Post-unlearning Performance
Zhang, Z., Bui, N., Verma, A., Ding, B., & Low, B. K. H. (2026) · Machine Learning · doi:10.1007/s10994-026-07117-8
Snail: Scheduler-Driven Backdoor Injection in Split Federated Learning via Gradual Perturbation
Jiang, Z., Balaji, S., Chen, Y., Xiao, Y., Wang, G., Ren, Y., & Wang, N. (2026) · IEEE Conference on Communications and Network Security · doi:10.1109/cns69863.2026.11720909
Vuseghesa, F. K., Messai, M.-L., & Bentayeb, F. (2026) · SN Computer Science · doi:10.1007/s42979-026-05397-4
SwarmGuard: Serverless IDS for Byzantine UAV Swarms Under Dual-Layer Attack
Oli, A., & Mahalal, E. (2026) · IEEE Conference on Communications and Network Security · doi:10.1109/cns69863.2026.11720887
Enang, I. J., Leevy, J. L., Billion-Polak, P., & Khoshgoftaar, T. M. (2026) · IEEE Access · doi:10.1109/access.2026.3743719
Zhang, L., Zhu, N., He, J., Fang, J., & Jurcut, A. D. (2026) · Cluster Computing · doi:10.1007/s10586-026-06674-w
DP-RADAR: Byzantine-Robust Federated Learning with Adaptive Privacy Budgeting
Chen, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6840-5_27
Haddad, A., Wajs, S. A., Habaebi, M. H., & Husaini, M. A. S. A. (2026) · Computers · doi:10.3390/computers15100726
Yan, W. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02316-7
Alsulami, A. A., Al-Haija, Q. S. A., Tayeb, A. J., Alakhtar, R. A., Alturki, B., Alsobhi, H., & Bukhari, A. A. (2026) · Bioengineering · doi:10.3390/bioengineering13111285
mmPoison: Targeted Clean-label Poisoning Attacks on mmWave-based Human Activity Recognition
Cao, H., Yu, J., Zhu, Y., Dai, H., & Kong, L. (2026) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3842721.3849653
Metadata
- Authors
- Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Truex, Stacey
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 480-501
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3087391814
- Cited by
- 927
- References
- 52
- Citations by year
- 2026: 124 · 2025: 219 · 2024: 243 · 2023: 160 · 2022: 113 · 2021: 62 · 2020: 6
- FWCI
- 32.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1564097)
- Directorate for Computer and Information Science and Engineering (1564097)
- 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 › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- data poisoning, federated learning, targeted attacks, malicious participants, mislabeled data, model updating
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