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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 701–800 of 929
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Zheng, T., Li, Y., Deng, B., & Li, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38404-1_25 · Cited by 2
Introduction to Federated Learning
Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_1 · Cited by 1
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital LearningPreprint
El-Bouri, R., Zhu, T., & Clifton, D. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01707 · Cited by 1
Learning-to-learn non-convex piecewise-Lipschitz functionsPreprint
Balcan, M.-F., Khodak, M., Sharma, D., & Talwalkar, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.08770 · Cited by 1
A Separation Result Between Data-oblivious and Data-aware Poisoning AttacksPreprint
Deng, S., Garg, S., Jha, S., Mahloujifar, S., Mahmoody, M., & Thakurta, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12020 · Cited by 2
FLAP: Federated Learning with Attack and Privacy AwarenessPreprint
Xue, W., Ahmed, N., Masood, R., Hu, W. S., Seneviratne, A. P., & Jha, S. K. (2022) · doi:10.36227/techrxiv.19766023.v1 · Cited by 1
Data Poisoning Attack Against Federated Learning Based Autonomous Driving ModelPreprint
Wang, S., Li, Q., Cui, Z., Hou, J., & Huang, C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4195982 · Cited by 1
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Ditzler, G., & Li, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4517547 · Cited by 3
Khorramfar, M. (2023) · doi:10.36939/ir.202308301626 · Cited by 1
Robust and Privacy-Preserving Federated Learning
Elhattab, F. (2023) · theses.fr (ABES) · doi:10.70675/062161c5z9cdez4465z92bezb97b4a23cb36 · Cited by 2
FLAP: Federated Learning with Attack and Privacy AwarenessPreprint
Xue, W., Ahmed, N., Masood, R., Hu, W. S., Seneviratne, A. P., & Jha, S. K. (2022) · doi:10.36227/techrxiv.19766023
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Loh, Y., Chen, Z., Zhao, Y., & Yu, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05061-9_12
Privacy Preservation using Federated Learning and Homomorphic Encryption: A Study
Ajay, D. M. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927802 · Cited by 1
Privacy-Preserving and Secure Divide-and-Conquer Learning
Brown, L. C., & Li, Q. (2022) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec54971.2022.00078
Targeted Clean-Label Poisoning Attacks on Federated Learning
Patel, A. S., & Singh, P. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23599-3_17
A Voting-Based Poisoning Attack to Federated Learning with Quantization
Ma, X., Wei, L., Zhang, B., Wang, Y., Zhang, C., & Li, Y. (2022) · International Conference on Hot Information-Centric Networking (HotICN) · doi:10.1109/hoticn57539.2022.10036222
FedTA: Locally-Differential Federated Learning with Top-k Mechanism and Adam Optimization
Li, Y., Wang, G., Peng, T., & Feng, G. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0272-9_26
Real-Time Data Cloud Transmission and Early Warning Algorithm for Outdoor Sports with Smart Glasses
Li, G. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134159
Going Haywire: False Friends in Federated Learning and How to Find Them
Aiken, W., Branco, P., & Jourdan, G.-V. (2023) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3579856.3595790 · Cited by 1
Porambage, P., Liyanage, M., & Sheikhi, S. (2023) · doi:10.1002/9781119875437.ch6
Trust Score-based Federated Learning to Defend against Data Poisoning Attacks
Ou, S., Jeong, S., & Kim, K. (2023) · Journal of Digital Contents Society · doi:10.9728/dcs.2023.24.6.1317
Detecting Low-Quality Models in Multidomain Federated Learning SystemsPreprint
Sameera, K. M., Sgueglia, A., Vinod, P., Rehiman, K. A. R., Visaggio, C. A., & Sorbo, A. D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4604411
An Efficient Privacy-Preserving and Verifiable Scheme for Federated LearningPreprint
Yang, X. S., Ma, M., & Tang, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4617673
Byzantine-Robust Federated Learning via Server-Side Mixtue of Experts
Fan, X., Shen, Z., Fan, W., Yang, K., & Li, J. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7022-3_4
Resilient machine learning methods for cyber-attack detection
Ongun, T. (2023) · doi:10.17760/d20486918 · Cited by 1
Joshi, I., Upadhya, P., Nayak, G. K., Schüffler, P. J., & Navab, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_29
Deep Federated Learning Based on Knowledge Distillation and Differential Privacy
Lin, H., Yu, F., & Wang, X. (2023) · doi:10.1002/9781119913924.ch5
Federated Learning for IoT Applications, Attacks and Defense Methods
Qu, Z., Duan, R., Liu, Y., & Lu, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-42637-7_9
An Efficient Privacy-Preserving and Verifiable Scheme for Federated LearningPreprint
Yang, X. S., Ma, M., & Tang, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4676342
Flex: Flexible Federated Learning FrameworkPreprint
Herrera, F., Jiménez-López, D., Argente-Garrido, A., Rodríguez-Barroso, N., Zuheros, C., Aguilera, I., Bello, B., García-Márquez, M., & Luzón, M. V. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4764504
Authentication in a Hyperconnected World: Challenges, Opportunities and Approaches
Lipps, C., Herbst, J., Reddy, R., Rüb, M., & Schotten, H. D. (2024) · International Conference on Cyber Warfare and Security · doi:10.34190/iccws.19.1.2070
SL-Defender: Decentralized Defense for Adversarial Robustness in Federated Learning
Justine, C., Manamohan, S., & Jiji, C. V. (2023) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/iccst59048.2023.10474244
RAFT: Evaluating Federated Learning Resilience Against Threats
Kumar, M., Agrawal, R., & Singh, P. (2023) · International Conference on Security of Information and Networks (SIN) · doi:10.1109/sin60469.2023.10474931
Machine learning frameworks in IoT systems: A survey, case study, and future research directions
Chen, Z., Tian, P., Qian, C., Liao, W., Hussaini, A., & Yu, W. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13462-3.00015-7
The Impact of Device Type Number on IoT Device Classification
Ergün, A. E., & Can, Ö. (2024) · Black Sea Journal of Engineering and Science · doi:10.34248/bsengineering.1353999
Li, S., Zhu, H., Wu, W. R., & Shen, X. (2024) · Wireless networks · doi:10.1007/978-3-031-57389-7_1
Blockchain-based Federated Learning via Model Training
Wang, R., & Wang, J. (2023) · International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) · doi:10.1109/iccbd-ai62252.2023.00045
The Cost of Privacy: A Comprehensive Analysis of the Security Issues in Federated Learning
Sundar, A. P., Li, F., Zou, X., & Gao, T. (2024) · Advances in information security · doi:10.1007/978-3-031-53510-9_14
Data Poisoning Attack Based on Privacy Reasoning and Countermeasure in Federated Learning
Lv, J., Xu, S., Ling, Y., Man, D., Han, S., & Yang, W. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00074
Blockchain-Based Federated Learning with Malicious Attacks in Fog Computing Networks
Huang, X., Li, W., Ren, Y., & Chen, Q. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-67162-3_4
Tang, B., & Li, X. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-58923-2_2
Unveiling Robustness of Spiking Neural Networks against Data Poisoning Attacks
Yadav, S., Pundhir, A., Raman, B., & Kumar, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650573
Verifiable Federated Learning Based on Data Service Quality
Xu, H., Feng, Y., & Xie, K. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667494
Federated Learning Defence Based on Label-Flipping Attacks
Chen, A., Mou, Y., Chen, G., Xu, J., & Yan, X. (2024) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1145/3690931.3691012
Credit-Based Client Selection for Resilient Model Aggregation in Federated Learning
Khorramfar, M., Mtawa, Y. A., Abusitta, A. H., & Halabi, T. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622828
Ti-skol: A Modular Federated Learning Framework Supporting Security Countermeasure Composition
Lacour, D. D., Lacoste, M., Mario Südholt, B., & Traoré, J. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825127
Membership Data Privacy Protection and Poisoning Detection Scheme for Federated Learning
Li, Y., Sun, Z., & Ma, L. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9412-6_44
MDMFL: Monitored Defence Mechanism for Federated Learning against Data Poisoning Attack
Sinha, I. K., Bangal, A., Singh, K. P., & Verma, S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725046
Multi-Granularity Federated Learning by Graph-Partitioning
Dai, Z., Zhao, Y., Qiu, C., Wang, X., Yao, H., & Niyato, D. T. (2024) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2024.3494765
Decentralized Edge-Based Detection of Label Flipping Attacks in Federated Learning
Alotaibi, N., Felemban, M., & Mahmood, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-75016-8_19
On Adversarial Training with Incorrect Labels
Zhao, B. Z. H., Lu, J., Zhou, X., Vatsalan, D., Ikram, M., & Kâafar, M. A. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0573-6_9
Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings
Plant, R., Giuffrida, M. V., Pitropakis, N., & Gkatzia, D. (2024) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3507565 · Cited by 1
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Sameera, K. M., Abhinav, M., Amal, P. P., Abhiram, T., Abishek, R. K., Amal, T., Anaina, P., Vinod, P., A., R. R. K., & Conti, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-80408-3_9
FL-ProtectX: Combating Label flipping in complex multi-agent environments
Zamani, M. A., Amiri, F., Jamshidi, P., Hosseini, S. M., & Yazdani, N. (2024) · International Symposium on Telecommunications · doi:10.1109/ist64061.2024.10843483
Ahmed, Y., Sverdlov, M., & Miratnia, N. (2024) · Intelligent Cybersecurity Conference (ICSC) · doi:10.1109/icsc63108.2024.10895779
SBA: A Swift and Stealthy Backdoor Attack Framework for Federated Learning
Wang, J., Wu, Z., Wang, Z., & Lu, D. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889426
Multi-Target Federated Backdoor Attack Based on Feature AggregationPreprint
Hao, L., Hao, K., Wei, B., & Tang, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5177766
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Bouharoun, M., & Erradi, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-3774-4_9
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Chen, D., Li, T., Cai, Y., Al-Huthaifi, R., & Huang, W. (2024) · International Conference on Industrial Automation, Robotics and Control Engineering (IARCE) · doi:10.1109/iarce64300.2024.00050
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SecureFed: Blockchain-Based Defence for Data Poisoning Attack in Federated Learning
Alsharidah, A. A., Jha, D. N., Wei, B. W., Solaiman, E., & Ranjan, R. K. (2024) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc63386.2024.00064
Federated Learning: An Overview of Attacks and Defense Methods
Sameera, K. M., Arikkat, D. R., Vinod, P., A., R. R. K., Aneez, A., & Conti, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-83157-7_14
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Bhatnagar, K., Chattanathan, S., Dang, A., Eranki, B., Rana, R., Sridhar, C., Vedam, S., Yao, A., & Stamp, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-83157-7_15 · Cited by 1
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Marinus, M., Tsouvalas, V., & Meratnia, N. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4836-8_13
Batista, J. P. C., Bastos, J. J. S., Fontes, R. D. R., Cerqueira, E., Mota, V. F. S., & Villaça, R. D. S. (2025) · doi:10.5753/sbrc.2025.5915
Resilient Distributed Optimization
Kuwaranancharoen, K., Xin, L., & Sundaram, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-14081-5.00072-6
LBRFL: Lightweight Privacy-Preserving Federated Learning with Byzantine-Robustness
Shen, P., Zhao, S., Xu, J., Zhao, C., Chen, Z., & Guo, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6606-5_3
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FLUDP: A Backdoor Attack Defense Framework in Federated Learning
Zhou, C., Yang, Y., & Zhang, L. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033546
On Defending against Label Flipping Poisoning Attack for Personalized Federated Learning
Chen, Y., Hung, H.-N., Yang, S., Chou, Y.-C., & Lin, P. (2025) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc65282.2025.11059648
Xhemrishi, M., Amat, A. G. I., & Pejó, B. (2025) · doi:10.1145/3709023.3737692
Data Leakage and Data Poisoning
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Joseph, J., & Mathew, T. E. (2025) · International Conference on Control, Communication and Computing (ICCC) · doi:10.1109/iccc64910.2025.11077213
EADRAN: An edge marketplace for federated learning
Cao, T.-D., Nguyen, T., Nguyen, M., Truong-Huu, T., & Truong, H. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108046
Data Poisoning in FL: Clipping Malicious Updates
Spathoulas, G. P., & Kollarou, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00642-4_14
Comparative Analysis of Machine Learning and Federated Learning in Healthcare
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A Decoupled Contrastive Learning Framework for Backdoor Defense in Federated Learning
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Poisoning-Resilient Federated Learning for MEC-IoT Environments Using Blockchain
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Chen, G., Zhang, X., Hu, X., Liu, Y., Yang, Y., & Wang, W. (2025) · Nature Communications · doi:10.1038/s41467-025-63036-7 · Cited by 1
Lightweight Local Differential Privacy For High-dimensional Data
Zheng, T., Zhao, X., Liu, Z., Song, C., Li, Y., & Huang, Y. (2025) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat66811.2025.11181983
FLdetox: Detoxify Persistent Backdoors in Federated Learning
Tang, W., Liu, C., Jiang, Y., Zhao, J., & Sun, P. (2025) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs63083.2025.00049
PAMUS: An Entropy-Loss-Based Poisoning Attack for Undermining Machine Unlearning
Jiang, X., Ma, Y., Xu, W., & Wen, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00828-2_7
Enhanced security verifiable secure aggregation scheme in federated learning
Yao, W., Han, Y., Zhou, T., & Wang, X. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02111-8
A Learning Behavior Based Framework for Secure Personalised Blockchain Federated Learning
Tang, Y., Zhang, Z., & Liu, Q. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4142-3_10
Towards Building Robust, Reliable and Private AI/ML Security Solutions in Open Radio Access Networks
Chatzimiltis, S., Shojafar, M., Mashhadi, M. B., & Tafazolli, R. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00157
A Federated Learning Data Poisoning Attacker Detection Method based on Credibility and Data Quality
Song, H., Shi, S., Zhao, X., Xu, C., Zhang, S., & Xu, X. (2025) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc66434.2025.00012
LightGuard: Adaptive TEE-GPU Sync for Real-Time Training Integrity
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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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