Data Poisoning Attacks Against Federated Learning Systems

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

Deep Leakage from Gradients

Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906

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

Untitled work

(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

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Cao, X., Fang, M., Liu, J., & Gong, N. Z. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24434 · Cited by 999

Federated learning for smart healthcare: A survey

Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827

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

A robust privacy-preserving federated learning model against model poisoning attacks

Yazdinejad, A., Dehghantanha, A., Karimipour, H., Srivastava, G., & Parizi, R. M. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3420126 · Cited by 380

ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169918 · Cited by 371

From distributed machine learning to federated learning: a survey

Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01664-x · Cited by 397

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342

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

Decentralized Federated Learning: A Survey and Perspective

Yuan, L., Wang, Z., Sun, L., Yu, P. S., & Brinton, C. G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3407584 · Cited by 365

Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

Miao, Y., Liu, Z., Li, H., Choo, K.-K. R., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3196274 · Cited by 303

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 survey of federated learning for edge computing: Research problems and solutions

Xia, Q., Ye, W., Tao, Z., Wu, J., & Li, Q. (2021) · High-Confidence Computing · doi:10.1016/j.hcc.2021.100008 · Cited by 310

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833647 · Cited by 292

Recent advances on federated learning: A systematic survey

Liu, B., Lv, N., Guo, Y., & Li, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128019 · Cited by 275

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

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

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 223

CONTRA: Defending Against Poisoning Attacks in Federated Learning

Awan, S., Luo, B., & Li, F. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-88418-5_22 · Cited by 182

Federated Learning: Opportunities and ChallengesPreprint

Mammen, P. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.05428 · Cited by 388

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

Emerging trends in federated learning: from model fusion to federated X learning

Ji, S., Tan, Y., Saravirta, T., Yang, Z. Q., Liu, Y., Vasankari, L., Pan, S., Long, G., & Walid, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02119-1 · Cited by 192

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

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

Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

Bao, G., & Guo, P. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00377-4 · Cited by 150

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

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

Detection and Mitigation of Label-Flipping Attacks in Federated Learning Systems with KPCA and K-Means

Li, D., Wong, W. E., Wang, W., Yao, Y., & Chau, M. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00081 · Cited by 115

Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Wang, W., Memon, F. H., Lian, Z., Yin, Z., Gadekallu, T. R., Pham, Q., Dev, K., & Su, C. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3116917 · Cited by 112

FL-Defender: Combating targeted attacks in federated learning

Jebreel, N. M., & Domingo-Ferrer, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110178 · Cited by 110

SecFedNIDS: Robust defense for poisoning attack against federated learning-based network intrusion detection system

Zhang, Z., Zhang, Y., Guo, D., Yao, L. Y., & Zhao, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.04.010 · Cited by 110

Threats to Training: A Survey of Poisoning Attacks and Defenses on Machine Learning Systems

Wang, Z., Ma, J., Wang, X., Hu, J., Qin, Z., & Ren, K. (2022) · ACM Computing Surveys · doi:10.1145/3538707 · Cited by 118

Clustered Vehicular Federated Learning: Process and Optimization

Taïk, A., Mlika, Z., & Cherkaoui, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3149860 · Cited by 107

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 114

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 145

SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning

Xiao, X., Tang, Z., Li, C., Xiao, B., & Li, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172310 · Cited by 99

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

A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges

Maniriho, P., Mahmood, A. N., & Chowdhury, M. J. M. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.030 · Cited by 97

Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems

Lu, R., Zhang, W., Wang, Y., Li, Q., Zhong, X., Yang, H., & Wang, D. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3240767 · Cited by 98

Toward Robust Hierarchical Federated Learning in Internet of Vehicles

Zhou, H., Zheng, Y., Huang, H., Shu, J., & Jia, X. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3243003 · Cited by 104

Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges

Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022) · Security and Communication Networks · doi:10.1155/2022/2886795 · Cited by 105

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

FLCert: Provably Secure Federated Learning Against Poisoning Attacks

Cao, X., Zhang, Z., Jia, J., & Gong, N. Z. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3212174 · Cited by 92

Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Yaacoub, J.-P. A., Noura, H., & Salman, O. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.001 · Cited by 106

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

Ioannou, I. I., Nagaradjane, P., Angın, P., Balasubramanian, P., Kavitha, K. J., Murugan, P., & Vassiliou, V. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.02.023 · Cited by 89

Privacy-preserving edge federated learning for intelligent mobile-health systems

Aminifar, A., Shokri, M., & Aminifar, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.035 · Cited by 106

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

A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

Zhang, Y., Zeng, D., Luo, J., Fu, X., Chen, G., Xu, Z., & King, I. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678181 · Cited by 99

CBRFL: A framework for Committee-based Byzantine-Resilient Federated Learning

Xu, G., Lei, L., Mao, Y., Li, Z., Chen, X., & Zhang, K. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104165 · Cited by 86

Selective knowledge sharing for privacy-preserving federated distillation without a good teacher

Shao, J., Wu, F., & Zhang, J. (2024) · Nature Communications · doi:10.1038/s41467-023-44383-9 · Cited by 93

Privacy-Preserving and Byzantine-Robust Federated Learning

Dong, C., Weng, J., Li, M., Liu, J.-N., Liu, Z., Cheng, Y., & Yu, S. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3264697 · Cited by 88

A Differentially Private Federated Learning Model against Poisoning Attacks in Edge Computing

Zhou, J., Wu, N., Wang, Y., Gu, S., Cao, Z., Dong, X., & Choo, K. R. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3168556 · Cited by 93

Model poisoning attack in differential privacy-based federated learning

Yang, M., Cheng, H., Chen, F., Liu, X., Wang, M., & Li, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.02.025 · Cited by 86

Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics

Mohammadi, S., Balador, A., Sinaei, S., & Flammini, F. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104918 · Cited by 93

Roadmap for edge AI: a Dagstuhl perspective

Ding, A. Y., Peltonen, E., Meuser, T., Aral, A., Becker, C., Dustdar, S., Hiessl, T., Kranzlmüller, D., Liyanage, M., Maghsudi, S., et al. (2022) · ACM SIGCOMM Computer Communication Review · doi:10.1145/3523230.3523235 · Cited by 81

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy

Zhang, Y., Zeng, D., Luo, J., Xu, Z., & King, I. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587681 · Cited by 89

3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Li, H., Ye, Q., Hu, H., Li, J., Wang, L., Fang, C., & Shi, J. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179401 · Cited by 84

LFighter: Defending against the label-flipping attack in federated learning

Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.019 · Cited by 129

Projected federated averaging with heterogeneous differential privacy

Liu, J., Lou, J., Xiong, L., Liu, J., & Meng, X. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3503585.3503592 · Cited by 81

ADAM: An adaptive DDoS attack mitigation scheme in software-defined cyber-physical system

Cai, T., Jia, T., Adepu, S., Li, Y., & Yang, Z. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3240586 · Cited by 73

A survey on federated learning in data mining

Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78

Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities

Awadallah, A., Eledlebi, K., Zemerly, M. J., Puthal, D., Damiani, E., Taha, K., Kim, T., Yoo, P. D., Choo, K. R., Yim, M., et al. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3442475 · Cited by 85

Integration of large language models and federated learning

Chen, C., Feng, X., Li, Y., Lyu, L., Zhou, J., Zheng, X., & Yin, J. (2024) · Patterns · doi:10.1016/j.patter.2024.101098 · Cited by 140

Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

Wei, W., & Liu, L. (2024) · ACM Computing Surveys · doi:10.1145/3645102 · Cited by 105

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

Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

Yu, Y., Liu, Q., Wu, L., Yu, R., Yu, S. L., & Zhang, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25611 · Cited by 70

Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

Sun, Y., Ochiai, H., & Esaki, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133819 · Cited by 77

Anomaly detection and defense techniques in federated learning: a comprehensive review

Zhang, C., Yang, S., Mao, L., & Ning, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10796-1 · Cited by 73

A survey of security threats in federated learning

Feng, Y., Guo, Y., Hou, Y., Wu, Y., Lao, M., Yu, T., & Liu, G. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01664-0 · Cited by 70

PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data

Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63

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

Using Highly Compressed Gradients in Federated Learning for Data Reconstruction Attacks

Yang, H., Ge, M., Xiang, K., & Li, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3227761 · Cited by 69

A Detailed Survey on Federated Learning Attacks and Defenses

Sikandar, H. S., Waheed, H., Tahir, S., Malik, S. U. R., & Rafique, W. (2023) · Electronics · doi:10.3390/electronics12020260 · Cited by 64

Learn to Forget: Machine Unlearning via Neuron Masking

Ma, Z., Liu, Y., Liu, X., Liu, J., Ma, J., & Ren, K. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3194884 · Cited by 90

Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

Shen, X., Liu, Y., Li, F., & Li, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288886 · Cited by 62

D2MIF: A Malicious Model Detection Mechanism for Federated-Learning-Empowered Artificial Intelligence of Things

Liu, W., Lin, H., Wang, X., Hu, J. W., Kaddoum, G., Piran, M. J., & Alamri, A. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3081606 · Cited by 58

Federated Learning Attacks and Defenses: A Survey

Chen, Y., Gui, Y., Lin, H., Gan, W., & Wu, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020431 · Cited by 69

Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority

Zhu, J., Wu, J., Bashir, A. K., Pan, Q., & Yang, W. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3276781 · Cited by 59

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

Impact on blockchain-based AI/ML-enabled big data analytics for Cognitive Internet of Things environment

Mitra, A., Bera, B., Das, A. K., Jamal, S. S., & You, I. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.10.010 · Cited by 59

Advances in Robust Federated Learning: A Survey With Heterogeneity Considerations

Chen, C., Liao, T., Deng, X., Wu, Z., Huang, S., & Zheng, Z. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3527202 · Cited by 68

Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions

Qammar, A., Ding, J., & Ning, H. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10098-w · Cited by 58

Dynamic defense against byzantine poisoning attacks in federated learning

Rodríguez-Barroso, N., Martínez‐Cámara, E., Luzón, M. V., & Herrera, F. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.003 · Cited by 71

Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

Wan, W., Hu, S., Lu, J., Zhang, L. Y., Jin, H., & He, Y. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/106 · Cited by 54

Piccolo: Exposing Complex Backdoors in NLP Transformer Models

Liu, Y., Shen, G., Tao, G., An, S., Ma, S., & Zhang, X. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833579 · Cited by 65

Serverless on Machine Learning: A Systematic Mapping Study

Barrak, A., Petrillo, F. D. S., & Jaafar, F. (2022) · IEEE Access · doi:10.1109/access.2022.3206366 · Cited by 53

Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges

Neto, E. C. P., Sadeghi, S., Zhang, X., & Dadkhah, S. (2023) · Applied Sciences · doi:10.3390/app13116497 · Cited by 53

Exploring privacy measurement in federated learning

Jagarlamudi, G. K., Yazdinejad, A., Parizi, R. M., & Pouriyeh, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05846-4 · Cited by 52

Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition

Li, Y., Wang, X., & An, L. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580795 · Cited by 62

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51

Detecting model misconducts in decentralized healthcare federated learning

Kuo, T.-T., & Pham, A. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104658 · Cited by 52

Blockchain-Enabled: Multi-Layered Security Federated Learning Platform for Preserving Data Privacy

Mahmood, Z., & Jusas, V. (2022) · Electronics · doi:10.3390/electronics11101624 · Cited by 51

Distributed and Secure Federated Learning for Wireless Computing Power Networks

Wang, P., Sun, W. Y., Zhang, H., Ma, W., & Zhang, Y. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3247859 · 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

Pocket diagnosis: Secure federated learning against poisoning attack in the cloud

Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K.-K. R., & Deng, R. H. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3090771 · Cited by 50

APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning

Chen, X., Yu, H., Jia, X., & Yu, X. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3315125 · Cited by 50

Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures

Yuan, W., Nguyen, Q. V. H., He, T., Chen, L., & Yin, H. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591722 · Cited by 50

Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

Nawshin, F., Gad, R., Ünal, D., Al-Ali, A. K., & Suganthan, P. N. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109233 · Cited by 56

Detection and Mitigation of Targeted Data Poisoning Attacks in Federated Learning

Erbil, P., & Gürsoy, M. E. (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/cy55231.2022.9927914 · Cited by 5

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

Data Poisoning Attacks on Federated Machine Learning

K Naresh, M. S. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15615280

Study of attacks on federated learning

Thung, J. C. (2021) · DR-NTU (Nanyang Technological University)

CONTRA: Defending Against Poisoning Attacks in Federated Learning

Awan, S., Luo, B., & Li, F. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-88418-5_22 · Cited by 182