Cited by 292Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataDigital Media Forensic Detection

The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.

PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Zhang, Jiale, Chen, Bing, Cheng, Xiang, Binh, Huynh Thi Thanh, Yu, Shui·2021·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.3023126

Open this paper in CoChat — free account

Abstract

Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Integrity signals

Registry

Crossref · 10.1109/jiot.2020.3023126

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
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
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956
A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
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

Show all 42 references

Sorted by citations

Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Ye, M., Fang, X., Du, B., Yuen, P. C., & Tao, D. (2023) · ACM Computing Surveys · doi:10.1145/3625558 · Cited by 709
Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
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
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 326
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning
Tian, Z., Cui, L., Liang, J., & Yu, S. (2022) · ACM Computing Surveys · doi:10.1145/3551636 · Cited by 293
Self-citation
Federated learning: Overview, strategies, applications, tools and future directions
Yürdem, B., Kuzlu, M., Güllü, M. K., Çatak, F. Ö., & Tabassum, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38137 · Cited by 342
A Privacy-Aware and Incremental Defense Method Against GAN-Based Poisoning Attack
Qiao, F., Li, Z., & Kong, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3263241 · Cited by 222
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
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 209
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
Tu, X., Zhu, K., Luong, N. C., Niyato, D. T., Zhang, Y., & Li, J. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3177522 · Cited by 181
Privacy and Security in Federated Learning: A Survey
Gosselin, R., Vieu, L., Loukil, F., & Benoît, A. (2022) · Applied Sciences · doi:10.3390/app12199901 · Cited by 178
Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
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 185
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Li, X., Qu, Z., Zhao, S., Tang, B., Lu, Z., & Liu, Y. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135422 · Cited by 158
Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey
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
“Adversarial attacks on WFL can be broadly classiﬁed into four categories: poisoning [29], inference attacks [30], Byzantine attacks [31], and backdoor attacks [32].”
BackgroundMethodInfluential
An overview of implementing security and privacy in federated learning
Hu, K., Gong, S., Zhang, Q., Seng, C., Xia, M., & Jiang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10846-8 · Cited by 175
Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions
Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130
Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions
Javeed, D., Saeed, M. S., Ahmad, I., Adil, M., Kumar, P., & Islam, A. K. M. N. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.023 · Cited by 143
Privacy-Preserving Federated Learning for Industrial Edge Computing via Hybrid Differential Privacy and Adaptive Compression
Jiang, B., Li, J., Wang, H. H., & Song, H. H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3131175 · Cited by 123
Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems
Kalapaaking, A. P., Khalil, I., & Yi, X. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3268186 · Cited by 122
PPFLHE: A privacy-preserving federated learning scheme with homomorphic encryption for healthcare data
Wang, B., Li, H., Guo, Y., & Wang, J. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110677 · Cited by 112
Advanced Deep Learning Models for 6G: Overview, Opportunities, and Challenges
Jiao, L., Shao, Y., Sun, L., Liu, F., Yang, S., Ma, W., Li, L., Liu, X., Hou, B., Zhang, X., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3418900 · Cited by 122
Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G
Lu, X., Xiao, L., Li, P., Ji, X., Xu, C., Yu, S., & Zhuang, W. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3224279 · Cited by 117
Self-citation
Data Quality Detection Mechanism Against Label Flipping Attacks in Federated Learning
Jiang, Y., Zhang, W., & Chen, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3249568 · Cited by 102
Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijji, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10417-3 · Cited by 100
A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions
Uddin, M. P., Xiang, Y., Hasan, M., Bai, J., Zhao, Y., & Gao, L. (2025) · ACM Computing Surveys · doi:10.1145/3727643 · Cited by 97
Trustworthy federated learning: privacy, security, and beyond
Chen, C., Liu, J., Tan, H., Li, X., Wang, K. I., Li, P., Sakurai, K., & Dou, D. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02285-2 · Cited by 110
A review on federated learning architectures for privacy-preserving AI: lightweight and secure cloud–Edge–End collaboration
Zhan, S., Huang, L., Luo, G., Zheng, S., Gao, Z., & Chao, H.-C. (2025) · Electronics · doi:10.3390/electronics14132512 · Cited by 82
Multitentacle Federated Learning Over Software-Defined Industrial Internet of Things Against Adaptive Poisoning Attacks
Li, G., Wu, J., Li, S., Yang, W., & Li, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3173996 · Cited by 84
Federated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79
Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks
Ferrag, M. A., Debbah, M. A., & Al-Hawawreh, M. S. (2023) · doi:10.1109/ccgridw59191.2023.00018 · Cited by 78
Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems
Liu, J., Nogueira, M., Fernandes, J., & Kantarcı, B. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3136132 · Cited by 84
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
DIGFuPAS: Deceive IDS with GAN and function-preserving on adversarial samples in SDN-enabled networks
Duy, P. T., Tien, L. K., Khoa, N. H., Hien, D. T. T., Nguyen, A. G.-T., & Pham, V.-H. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102367 · Cited by 70
Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey
Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82
RETRACTED: SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising
Poongodi, M., Walia, R., Iwendi, C., Ahanger, T. A., Suganthi, S. T., Kamruzzaman, M. M., Bourouis, S., Alhakami, W., & Hamdi, M. (2022) · Expert Systems · doi:10.1111/exsy.13072 · Cited by 63
Retracted
SVeriFL: Successive verifiable federated learning with privacy-preserving
Gao, H., He, N., & Gao, T. (2022) · Information Sciences · doi:10.1016/j.ins.2022.11.124 · Cited by 61
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
Generative Adversarial Networks: A Survey on Attack and Defense Perspective
Zhang, C., Yu, S., Tian, Z., & Yu, J. J. Q. (2023) · ACM Computing Surveys · doi:10.1145/3615336 · Cited by 69
Self-citation
Deep learning for intrusion detection in emerging technologies: a comprehensive survey and new perspectives
Neto, E. C. P., Iqbal, S., Buffett, S., Sultana, M., & Taylor, A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11346-z · Cited by 84
Anonymous and Efficient Authentication Scheme for Privacy-Preserving Distributed Learning
Jiang, Y., Zhang, K., Qian, Y., & Zhou, L. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3181848 · Cited by 58
ADFL: Defending backdoor attacks in federated learning via adversarial distillation
Zhu, C., Zhang, J., Sun, X., Chen, B., & Meng, W. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103366 · Cited by 61
Self-citation
Federated Learning Under Attack: Exposing Vulnerabilities Through Data Poisoning Attacks in Computer Networks
Nowroozi, E., Haider, I., Taheri, R., & Conti, M. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3525554 · Cited by 57
A comprehensive review of federated learning for COVID‐19 detection
Naz, S., Phan, K. T., & Chen, Y. P. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22777 · Cited by 59
RobustFL: Robust Federated Learning Against Poisoning Attacks in Industrial IoT Systems
Zhang, J., Ge, C., Hu, F., & Chen, B. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3132954 · Cited by 52
Self-citation
Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things
Liu, M., Zhang, H., Liu, Z., & Zhao, N. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3179491 · Cited by 54
A survey on vulnerability of federated learning: A learning algorithm perspective
Xie, X., Hu, C. X., Ren, H., & Deng, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127225 · Cited by 55
Federated Threat-Hunting Approach for Microservice-Based Industrial Cyber-Physical System
Abdel-Basset, M., Hawash, H., & Sallam, K. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3091150 · Cited by 47
A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems
Sharma, A., Singh, S., Chhabra, A., Kumar, S., Arya, V., & Moslehpour, M. (2023) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.334711 · Cited by 49
Two-phase Defense Against Poisoning Attacks on Federated Learning-based Intrusion Detection
Lai, Y., Lin, J.-Y., Lin, Y., Hwang, R., Lin, P.-C., Wu, H.-K., & Chen, C.-K. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103205 · Cited by 53
Adaptive and Dynamic Security in AI-Empowered 6G: From an Energy Efficiency Perspective
Shen, S., Yu, C., Zhang, K., Ni, J., & Ci, S. (2021) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.101.2000090 · Cited by 43
Communication-efficient federated continual learning for distributed learning system with Non-IID data
Zhang, Z., Zhang, Y., Guo, D., Zhao, S., & Zhu, X. (2022) · Science China Information Sciences · doi:10.1007/s11432-020-3419-4 · Cited by 42
Communications Security in Industry X: A Survey
Ahmad, I., Rodriguez, F., Kumar, T., Suomalainen, J., Jagatheesaperumal, S. K., Walter, S., Asghar, M. Z., Li, G., Papakonstantinou, N., Ylianttila, M., et al. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3356076 · Cited by 43
Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
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
Federated Transfer–Ordered–Personalized Learning for Driver Monitoring Application
Yuan, L., Su, L., & Wang, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3279273 · Cited by 40
A review on client-server attacks and defenses in federated learning
Sharma, A., & Marchang, N. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103801 · Cited by 46
FLPurifier: Backdoor Defense in Federated Learning via Decoupled Contrastive Training
Zhang, J., Zhu, C., Sun, X., Ge, C., Chen, B., Susilo, W., & Yu, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3384846 · Cited by 42
Self-citation
Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions
Mahbub, M., & Shubair, R. M. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103726 · Cited by 42
Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems
Yang, L. T., Zhao, R., Liu, D., Lu, W., & Deng, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3282264 · Cited by 36
A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges
Narula, M., Meena, J., & Vishwakarma, D. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108128 · Cited by 38
Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques
Hamouda, D., Ferrag, M. A., Benhamida, N., Séridi, H., & Ghanem, M. C. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101149 · Cited by 40
Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse
Zainudin, A., Putra, M. A. P., Alief, R. N., Akter, R., Kim, D.-S., & Lee, J.-M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3364247 · Cited by 35
SIDS: A federated learning approach for intrusion detection in IoT using Social Internet of Things
Amiri-Zarandi, M., Dara, R. A., & Lin, X. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110005 · Cited by 37
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 34
Leveraging AI for Network Threat Detection—A Conceptual Overview
Paracha, M. A., Jamil, S. U., Shahzad, K., Khan, M. A., & Rasheed, A. (2024) · Electronics · doi:10.3390/electronics13234611 · Cited by 42
Privacy-Preserving Multi-Source Domain Adaptation for Medical Data
Han, T., Gong, X., Feng, F., Zhang, J., Sun, Z., & Zhang, Y. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3175071 · Cited by 31
Self-citation
Security of federated learning for cloud‐edge intelligence collaborative computing
Yang, J., Zheng, J., Zhang, Z., Chen, Q., Wong, D. S., & Li, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22992 · Cited by 32
Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud–edge–client learning for image classification
Zhao, Y., Zhang, J., & Cao, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110072 · Cited by 30
Self-citation
Privacy and Security Challenges in Federated Learning for UAV Systems: A Systematic Review
Farsi, A. A., Khan, A., Mughal, M. R., & Bait‐Suwailam, M. M. (2025) · IEEE Access · doi:10.1109/access.2025.3570538 · Cited by 42
Privacy-preserved learning from non-i.i.d data in fog-assisted IoT: A federated learning approach
Abdel‐Basset, M., Hawash, H., Moustafa, N., Razzak, I., & Elfattah, M. A. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.12.013 · Cited by 35
BFKD: Blockchain-Based Federated Knowledge Distillation for Aviation Internet of Things
Deng, W., Li, X., Xu, J., Li, W., Zhu, G., & Zhao, H. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3474710 · Cited by 28
Toward Federated Learning Models Resistant to Adversarial Attacks
Hu, F., Zhou, W., Liao, K., Li, H., & Tong, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3272334 · Cited by 29
FedTor: An Anonymous Framework of Federated Learning in Internet of Things
Chen, Y., Su, Y., Zhang, M., Chai, H., Wei, Y., & Yu, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3162826 · Cited by 28
Self-citation
Robust and Secure Federated Learning Against Hybrid Attacks: A Generic Architecture
Hao, X., Lin, C., Dong, W., Huang, X., & Xiong, H. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3336521 · Cited by 27
Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack
Rezaie, H., Taheri, R., Jordanov, I., & Shojafar, M. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09963-8 · Cited by 25
Federated Learning-Based Prediction of Energy Consumption from Blockchain-Based Black Box Data for Electric Vehicles
Park, J.-H., & Joe, I. (2024) · Applied Sciences · doi:10.3390/app14135494 · Cited by 23
Blockchain-Enabled and Multisignature-Powered Verifiable Model for Securing Federated Learning Systems
Kalapaaking, A. P., Khalil, I., & Atiquzzaman, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3289832 · Cited by 24
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups
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 26
Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective
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 24
Privacy and security vulnerabilities in edge intelligence: An analysis and countermeasures
Shafee, A. A., Hasan, S. R., & Awaad, T. A. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110146 · Cited by 25
Federated Split Learning Model for Industry 5.0: A Data Poisoning Defense for Edge Computing
Khan, F., Kumar, R. L., Abidi, M. H., Kadry, S., Alkhalefah, H., & Aboudaif, M. K. (2022) · Electronics · doi:10.3390/electronics11152393 · Cited by 21
GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems
Psychogyios, K., Velivassaki, T.-H., Bourou, S., Voulkidis, A. C., Skias, D., & Zahariadis, T. B. (2023) · Electronics · doi:10.3390/electronics12081805 · Cited by 24
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications
Li, G., Zhao, Y., & Li, Y. (2023) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc55385.2023.10118885 · Cited by 21
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
Mei, H., Li, G., Wu, J., & Zheng, L. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191260 · Cited by 24
VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks
Huang, Y., Yang, G., Zhou, H., Dai, H., Yuan, D., & Yu, S. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103562 · Cited by 21
Self-citation
Clean‐label poisoning attacks on federated learning for IoT
Yang, J., Zheng, J., Baker, T., Tang, S., Tan, Y., & Zhang, Q. (2023) · Expert Systems · doi:10.1111/exsy.13161 · Cited by 20
Leverage Variational Graph Representation for Model Poisoning on Federated Learning
Li, K., Yuan, X., Zheng, J., Ni, W., Dressler, F., & Jamalipour, A. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3394252 · Cited by 20
Backdoor attacks and defense mechanisms in federated learning: A survey
Li, Z., Lan, J., Yan, Z., & Erol Gelenbe, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103248 · Cited by 20
ROFED-LLM: Robust Federated Learning for Large Language Models in Adversarial Wireless Environments
Wang, H., Yin, Z., Chen, B., Zeng, Y., Yan, X., Zhou, C., & Li, A. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3590975 · Cited by 20
Self-citation
NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory
Deng, H., Hu, J., Sharma, R., Mo, M., & Ren, Y. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.04.026 · Cited by 19
Evaluation of data poisoning attacks on federated learning-based network intrusion detection system
Zhang, Y., Zhang, Y., Zhang, Z., Bai, H., Zhong, T., & Song, M. N. (2022) · 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/hpcc-dss-smartcity-dependsys57074.2022.00330 · Cited by 18
DFL: High-Performance Blockchain-Based Federated Learning
Tian, Y., Guo, Z., Zhang, J., & Al-Ars, Z. (2023) · Distributed Ledger Technologies Research and Practice · doi:10.1145/3600225 · Cited by 20
Self-citation
A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment
Arif, M., & Rashid, M. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.062819 · Cited by 21
DNS Rebinding Threat Modeling and Security Analysis for Local Area Network of Maritime Transportation Systems
He, X., Wang, J., Liu, J., Ding, W., Han, Z., Wang, B., Nebhen, J., & Wang, W. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3135197 · Cited by 18
Federated Learning-Based Intrusion Detection in the Context of IIoT Networks: Poisoning Attack and Defense
Vy, N. C., Quyen, N. H., Duy, P. T., & Pham, V.-H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92708-0_8 · Cited by 18
Garbage In, Garbage Out: Poisoning Attacks Disguised With Plausible Mobility in Data Aggregation
Zhao, P., Jiang, H., Li, J., Xiao, Z. Q., Liu, D., Ren, J., & Guo, D. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3103919 · Cited by 17
Robust Federated Learning With Noisy Labeled Data Through Loss Function Correction
Chen, L., Ang, F., Chen, Y., & Wang, W. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3227287 · Cited by 17
Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues
Afzal, M. U., Abdellatif, A. A., Zubair, M. F., Mehmood, M. Q., & Massoud, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3323932 · Cited by 23
A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs
Feng, X., Wang, X., Liu, H., Yang, H., & Wang, L. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3369942 · Cited by 18
Infrastructure Assisted Autonomous Driving: Research, Challenges, and Opportunities
George, R., Clancy, J., Brophy, T., Sistu, G., O'Grady, W., Chandra, S., Collins, F., Mullins, D., Jones, E., Deegan, B. M., et al. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3542213 · Cited by 19

Authors: Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S.
Affiliations: Nanjing University of Aeronautics and Astronautics (CN)
School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
University of Technology Sydney (AU)
ORCID: Zhang, Jiale
Chen, Bing
Cheng, Xiang
Binh, Huynh Thi Thanh
Yu, Shui
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-03-01
Volume: 8
Issue: 5
Pages: 3310-3322
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.3023126
OpenAlex: W3084847664
Cited by: 291
References: 42
Citations by year: 2026: 34 · 2025: 61 · 2024: 72 · 2023: 63 · 2022: 43 · 2021: 18
FWCI: 12.18
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
UTS ePRESS (University of Technology Sydney) (submittedVersion)
Funders: National Key Research and Development Program of China (2019YFB2102000)
National Natural Science Foundation of China (61672283)
Science and Technology on Avionics Integration Laboratory and the National Aeronautical Science Foundation of China (20175552039)
Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18_0308)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Keywords: federated learning, generative adversarial networks, data poisoning, edge computing, Internet of Things, label flipping, backdoor attacks, privacy leakage

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks