Data Poisoning Attacks Against Federated Learning Systems

ImageNet: A large-scale hierarchical image database

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Learning Multiple Layers of Features from Tiny Images

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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Towards Federated Learning at Scale: System Design

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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

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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

MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing

Wang, R., Xu, C., Zhang, S., Ye, F., Tang, Y., Tang, S. H., Zhang, H., Du, W., & Zhang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-53431-x · Cited by 12

Reliable federated learning based on delayed gradient aggregation for intelligent connected vehicles

Yang, Z., Cheng, C., Li, Z., Wang, R., & Zhang, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109719 · Cited by 14

FLEX: Flexible Federated Learning Framework

Herrera, F., Jiménez-López, D., Argente-Garrido, A., Rodríguez-Barroso, N., Zuheros, C., Aguilera-Martos, I., Bello, B. L. M., García-Márquez, M., & Luzón, M. V. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102792 · Cited by 14

Vertical Federated Unlearning via Backdoor Certification

Han, M., Zhu, T., Zhang, L., Huo, H., & Zhou, W. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3536312 · Cited by 12

Venomave: Targeted Poisoning Against Speech Recognition

Aghakhani, H., Schönherr, L., Eisenhofer, T., Kolossa, D., Holz, T., Kruegel, C., & Vigna, G. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00035 · Cited by 16

LSBlocFL: A secure federated learning model combining blockchain and lightweight cryptographic solutions

Deng, S., Zhang, J., Li, T., Jiang, X., & Wang, F. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108986 · Cited by 13

Demystifying Data Poisoning Attacks in Distributed Learning as a Service

Wei, W., Chow, K.-H., Wu, Y., & Liu, L. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3341951 · Cited by 14

Covert Channel Attack to Federated Learning Systems.Preprint

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2021) · arXiv (Cornell University) · Cited by 12

Surveying federated learning approaches through a multi-criteria categorization

Caruccio, L., Cimino, G., Deufemia, V., Iuliano, G., & Stanzione, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16050-0 · Cited by 11

MITDBA: Mitigating Dynamic Backdoor Attacks in Federated Learning for IoT Applications

Wang, Y., Zhai, D., Han, D., Guan, Y., & Xia, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325634 · Cited by 12

Systematic Analysis of Label-flipping Attacks against Federated Learning in Collaborative Intrusion Detection Systems

Lavaur, L., Busnel, Y., & Autrel, F. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3670434 · Cited by 12

Opportunities and Challenges of Using Artificial Intelligence in Securing Cyber-Physical Systems

Nweke, L. O., & Yayilgan, S. Y. (2024) · Springer eBooks · doi:10.1007/978-3-031-57452-8_7 · Cited by 12

Byzantine-Robust and Privacy-Preserving Federated Learning With Irregular Participants

Chen, Y., Tan, W., Zhong, Y., Kang, Y., Yang, A., & Weng, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3434660 · Cited by 11

A Secure Object Detection Technique for Intelligent Transportation Systems

Mia, M. J., & Amini, M. H. (2024) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2024.3440876 · Cited by 11

Machine Learning Security: Threat Model, Attacks, and Challenges

Koball, C., Wang, Y., Rimal, B. P., & Vaidyan, V. M. (2024) · Computer · doi:10.1109/mc.2024.3396357 · Cited by 11

Fed-LSAE: Thwarting poisoning attacks against federated cyber threat detection system via Autoencoder-based latent space inspection

Luong, T. D., Tien, V. M., Quyen, N. H., Hien, D. T. T., Duy, P. T., & Pham, V.-H. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103916 · Cited by 11

Federated Learning: A Comparative Study of Defenses Against Poisoning Attacks

Carvalho, I., Huff, K., Gruenwald, L., & Bernardino, J. (2024) · Applied Sciences · doi:10.3390/app142210706 · Cited by 11

DPAD: Data Poisoning Attack Defense Mechanism for federated learning-based system

Basak, S., & Chatterjee, K. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109893 · Cited by 16

A Data Poisoning Resistible and Privacy Protection Federated-Learning Mechanism for Ubiquitous IoT

Chen, G., Li, X., You, L., Abdelmoniem, A. M., Zhang, Y., & Yuen, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3514637 · Cited by 11

FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data

Zhao, Z., Li, A., Li, R., Yang, L., & Xu, X. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3547751 · Cited by 11

M3D-FL: Multi-layer Malicious Model Detection for Federated Learning in IoT networks

Atia, O. B., Samara, M. A., Bennis, I., Abouaïssa, A., Gaber, J., & Lorenz, P. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104444 · Cited by 11

A federated learning attack method based on edge collaboration via cloud

Yang, J., Baker, T., Gill, S. S., Yang, X., Han, W., & Li, Y. (2024) · Software: Practice and Experience · doi:10.1002/spe.3180 · Cited by 11

Survey of recommender systems based on federated learning

梁, 锋., 羊, 恩., Pan, W., 杨, 强., & 明, 仲. (2021) · Scientia Sinica Informationis · doi:10.1360/ssi-2021-0329 · Cited by 11

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Altoub, M., Alqurashi, F. A., Yiğitcanlar, T., Corchado, J. M., & Mehmood, R. (2022) · Applied Sciences · doi:10.3390/app122111053 · Cited by 13

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence.Preprint

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2021) · arXiv (Cornell University) · Cited by 11

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic ReviewPreprint

Ali, A., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2021) · doi:10.36227/techrxiv.14945433 · Cited by 13

A Survey of Security Protection Methods for Deep Learning Model

Peng, H., Bao, S., & Li, L. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3314398 · Cited by 11

A privacy preserving multi-center federated learning framework for district heating forecast

Dai, K., González, E. F., & García-Betances, R. I. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115164 · Cited by 10

Differential private federated learning with per-sample adaptive clipping and layer-wise gradient perturbation

Yuan, J., & Chen, Y. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111139 · Cited by 12

Federated Knowledge Recycling: Privacy-preserving synthetic data sharing

Lomurno, E., & Matteucci, M. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.02.030 · Cited by 10

Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review

Ieropoulos, V., Anthi, E., Spyridopoulos, T., Burnap, P., Mavromatis, I., Khan, A., & Carnelli, P. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104127 · Cited by 11

GRAF-IDS: graph-based clustering as aggregation for federated intrusion detection system in IoT network

Rezaie, H., Taheri, R., Shojafar, M., & Foh, C. H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11385-1 · Cited by 10

Computationally Efficient Auto-Weighted Aggregation for Heterogeneous Federated Learning

Talukder, Z., & Islam, M. A. (2022) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge55608.2022.00015 · Cited by 10

Privacy-Preserving Federated Recurrent Neural Networks

Sav, S., Diaa, A., Pyrgelis, A., Bossuat, J.-P., & Hubaux, J. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0122 · Cited by 17

STDLens: Model Hijacking-Resilient Federated Learning for Object Detection

Chow, K.-H., Liu, L., Wei, W., Ilhan, F., & Wu, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01568 · Cited by 10

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning

Asif, H., Min, S., Wang, X., & Vaidya, J. (2024) · IEEE Transactions on Privacy · doi:10.1109/tp.2024.3392721 · Cited by 11

CELEST: Federated Learning for Globally Coordinated Threat Detection

Ongun, T., Boboila, S., Oprea, A., Eliassi‐Rad, T., Hiser, J. D., & Davidson, J. W. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3614442 · Cited by 10

DistFL: Distribution-aware Federated Learning for Mobile ScenariosPreprint

Liu, B., Cai, Y., Zhang, Z., Li, Y., Wang, L., Li, D., Guo, Y., & Chen, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11619 · Cited by 10

Defending against Label-Flipping Attacks in Federated Learning Systems with UMAPPreprint

Upreti, D., Kim, H., Yang, E., & Seo, C. (2022) · Research Square · doi:10.21203/rs.3.rs-1984301/v1 · Cited by 10

Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare

Zhu, X., Lai, T., & Li, H. (2024) · Applied Sciences · doi:10.3390/app14125247 · Cited by 10

Secure and Efficient Federated Learning Against Model Poisoning Attacks in Horizontal and Vertical Data Partitioning

Yu, C., Meng, Z., Zhang, W., Lei, L., Ni, J., Zhang, K., & Hai, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3486028 · Cited by 9

Federated Learning With Security Authentication and Traceability of Poisoning by Embedded Message Authentication Code

Ke, Y., Zhang, M., Liu, J., Han, Y., & Liu, W. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3557199 · Cited by 9

EARVP: Efficient Aggregation for Federated Learning With Robustness, Verifiability, and Privacy

Yan, C., Lu, X., Lió, P., & Hui, P. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3576008 · Cited by 10

Federated Learning for 6G Security: A Survey on Threats, Solutions, and Research Directions

de Alwis, C., Aouedi, O., Xu, J., Wang, S., Siriwardhana, Y., Hewa, T. M., Zeydan, E., Sandeepa, C., & Liyanage, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3663434 · Cited by 16

Towards Privacy-Preserving Federated Neuromorphic Learning via Spiking Neuron Models

Han, B., Fu, Q., & Zhang, X. (2023) · Electronics · doi:10.3390/electronics12183984 · Cited by 9

Efficient and persistent backdoor attack by boundary trigger set constructing against federated learning

Yang, D., Luo, S., Zhou, J., Pan, L., Yang, X., & Xing, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119743 · Cited by 9

VFedAD: A Defense Method Based on the Information Mechanism Behind the Vertical Federated Data Poisoning Attack

Lai, J., Wang, T., Chen, C., Li, Y., & Zheng, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615106 · Cited by 9

Comments on “Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT”

Sui, Z., Sun, Y., Zhu, J., & Chen, F. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358302 · Cited by 9

CoCFL: A Lightweight Blockchain-based Federated Learning Framework in IoT Context

Wang, J., Shi, Y., Hu, D., Li, K., & Liu, X. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00104 · Cited by 9

The More, the Better

Drichel, A., Holmes, B., von Brandt, J., & Meyer, U. (2021) · doi:10.1145/3474374.3486915 · Cited by 11

Computer Security – ESORICS 2020

(2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59013-0 · Cited by 9

Client-specific Property Inference against Secure Aggregation in Federated Learning

Kerkouche, R., Ács, G., & Fritz, M. (2023) · Workshop on Privacy in the Electronic Society · doi:10.1145/3603216.3624964 · Cited by 9

Exploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things

Li, K., Yuan, X., Zheng, J., Ni, W., & Guizani, M. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc58020.2023.10183217 · Cited by 8

Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution

Alharbi, E., Marcolino, L. S., Gouglidis, A., & Ni, Q. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230257 · Cited by 8

Low dimensional secure federated learning framework against poisoning attacks

Erdöl, E. S., Üstübioğlu, B., Erdöl, H., & Ulutaş, G. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.017 · Cited by 8

Byzantine-Robust Hierarchical Aggregation for Cross-Device Federated Learning in Consumer IoT

Liu, J., Wu, Y., Wei, D., Sun, R. X., Xu, G., Liu, L., & Wu, C. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3450649 · Cited by 11

Federated Learning with Dual Attention for Robust Modulation Classification under Attacks

Zhang, H., Elsayed, M., Bavand, M., Gaigalas, R., Ozcan, Y., & Erol‐Kantarci, M. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622317 · Cited by 8

Byzantine-Robust Multimodal Federated Learning Framework for Intelligent Connected Vehicle

Wu, N., Lin, X., Lu, J., Zhang, F., Chen, W., Tang, J., & Xiao, J. (2024) · Electronics · doi:10.3390/electronics13183635 · Cited by 11

Harmonizing Global and Local Class Imbalance for Federated Learning

Zhu, J., Zheng, H., Xu, W., Wang, H., He, Z., Liu, Y., Wang, S., & Sun, Q. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3476340 · Cited by 8

Edge-Assisted Label-Flipping Attack Detection in Federated Learning

Alotaibi, N., Felemban, M., & Mahmood, S. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3496872 · Cited by 9

DUPS: Data poisoning attacks with uncertain sample selection for federated learning

Zhang, H., Wang, K.-X., Liang, X.-Y., & Yu, Y.-F. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110909 · Cited by 8

Label flipping attacks in hierarchical federated learning for intrusion detection in IoT

Ennaji, E. M., Hajla, S. E., Maleh, Y., Mounir, S., & Ouazzane, K. (2024) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2024.2434586 · Cited by 8

Poisoning Attacks to Knowledge Distillation-Based Federated Learning Under Robust Aggregation Rules

Pang, X., Wang, Z., Liu, D., Hu, J., Sun, P., Luo, M., & Ren, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3586491 · Cited by 8

A defense method against multi-label poisoning attacks in federated learning

Ma, W., Zhao, Q., & Tian, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-09672-x · Cited by 11

Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes

Ovi, P. R., & Gangopadhyay, A. (2025) · Electronics · doi:10.3390/electronics14152970 · Cited by 8

An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems

Wang, S., & Zuccon, G. (2023) · International Conference on the Theory of Information Retrieval · doi:10.1145/3578337.3605117 · Cited by 11

MEDIC: Remove Model Backdoors via Importance Driven Cloning

Xu, Q., Tao, G., Honorio, J., Liu, Y., An, S., Shen, G., Cheng, S., & Zhang, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01962 · Cited by 9

FedSwarm: An Adaptive Federated Learning Framework for Scalable AIoT

Du, H., Ni, C., Cheng, C., Xiang, Q., Chen, X., & Liu, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3321325 · Cited by 8

A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward

Majeed, A., & Hwang, S. O. (2024) · IEEE Access · doi:10.1109/access.2024.3413069 · Cited by 12

Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning

Zhang, L., Fan, L., Luo, Y., & Duan, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3523059 · Cited by 8

PoisonHD: Poison Attack on Brain-Inspired Hyperdimensional Computing

Wang, R., & Jiao, X. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774641 · Cited by 8

Blockchain-enabled Efficient and Secure Federated Learning in IoT and Edge Computing Networks

Mallah, R. A., Lopez, D. L., & Halabi, T. (2023) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/icnc57223.2023.10074277 · Cited by 8

Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints

Li, M., Wan, W., Lu, J., Hu, S., Shi, J., Zhang, L. Y., Zhou, M., & Zheng, Y. (2022) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn57253.2022.00040 · Cited by 8

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning ApplicationsPreprint

Raza, A., Tran, K. P., Koehl, L., Li, S., & Tran, K. D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4811423 · Cited by 8

SAFe‐Health: Guarding federated learning‐driven smart healthcare with federated defense averaging against data poisoning

Mali, B., Singh, P. K., & Mazumdar, N. (2024) · Security and Privacy · doi:10.1002/spy2.403 · Cited by 7

Poisoning Attacks on Federated Learning for Autonomous Driving

Garg, S., Jönsson, H., Kalander, G., Nilsson, A., Pirange, B., Valadi, V., & Östman, J. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208002 · Cited by 9

Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategies

Kaushal, V., & Sharma, S. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02339-z · Cited by 9

Robust hierarchical federated learning with dual-layer filtering mechanism

Yang, C., Liang, W., Chen, Y., Xiao, J., He, D., Liu, T., Wang, J., & Tolba, A. K. R. (2025) · International Journal of Advanced Robotic Systems · doi:10.1177/17298806251322260 · Cited by 8

Adapting Aggregation Rule for Robust Federated Learning under Dynamic Attacks

Hu, C., Zhang, M., Li, N., Li, J., Yang, Z., Hassan, M. U., & Tei, K. (2025) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1109/seams66627.2025.00026 · Cited by 7

FLCom: Robust federated learning against strong model poisoning attacks

Li, Y., Xu, J., & Yang, D. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111442 · Cited by 9

Resilient Federated Learning for Vehicular Networks: A Digital Twin and Blockchain-Empowered Approach

Li, J., Zheng, C., & Chen, Z. (2025) · Future Internet · doi:10.3390/fi17110505 · Cited by 7

A Survey on Security and Privacy in Federated Learning-Based Intrusion Detection Systems for 5G and Beyond Networks

Rezaie, H., Taheri, R., Nowroozi, E., Hajizadeh, M., Shiaeles, S. N., & Bauschert, T. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3644477 · Cited by 11

The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research

Pirmani, A., Brouwer, E. D., Geys, L., Parciak, T., Moreau, Y., & Peeters, L. M. (2023) · JMIR Medical Informatics · doi:10.2196/48030 · Cited by 8

Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection

Upreti, D., Kim, H., Yang, E., & Seo, C. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i1.pp459-466 · Cited by 17

FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics

Liu, S., Xi, X., Wang, M., Wu, F., Ji, Y., Zhu, C., & Zhang, Q. (2023) · Applied Sciences · doi:10.3390/app14010351 · Cited by 7

Data Poisoning Attacks and Mitigation Strategies on Federated Support Vector Machines

Mouri, I. J., Ridowan, M., & Adnan, M. A. (2024) · SN Computer Science · doi:10.1007/s42979-023-02556-9 · Cited by 7

AIFL: Ensuring Unlinkable Anonymity and Robust Incentive in Cross-Device Federated Learning

Chen, X., Gao, Y., & Deng, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3417003 · Cited by 8

BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning

Liu, Y., Wang, C., & Yuan, X. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671879 · Cited by 7

Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment

Chung, H., & Lee, J. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-73863-1 · Cited by 9

FairGuard: A Fairness Attack and Defense Framework in Federated Learning

Sheng, X., Yang, Z., & Bao, W. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3520134 · Cited by 7

Are You a Good Client? Client Classification in Federated Learning

Jeong, H., An, J., & Jeong, J. P. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9620836 · Cited by 7

ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems

Pang, Q., Yuan, Y., Wang, S., & Zheng, W. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179446 · Cited by 19

Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance

Lee, Y., Park, S., & Kang, J. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635545 · Cited by 7

SecBFL-IoV: A Secure Blockchain-Enabled Federated Learning Framework for Resilience Against Poisoning Attacks in Internet of Vehicles

Ullah, I., Deng, X., Pei, X., & Mushtaq, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8487-5_29 · Cited by 7

A highly efficient, confidential, and continuous federated learning backdoor attack strategy

Cao, J., & Zhu, L. (2022) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3529836.3529845 · Cited by 7

Auction Based Clustered Federated Learning in Mobile Edge Computing SystemPreprint

Lu, R., Zhang, W., Li, Q., Zhong, X., & Vasilakos, A. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07150 · Cited by 9

EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature SymmetryPreprint

Liu, Y., Shen, G., Tao, G., Wang, Z., Ma, S., & Zhang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08820 · Cited by 9

A Survey on Federated Learning PoisoningAttacks and DefensesPreprint

Liang, J., & Rong, W. (2022) · Research Square · doi:10.21203/rs.3.rs-1900743/v1 · Cited by 15

Local perturbation-based black-box federated learning attack for time series classification

Chen, S., Yuan, J., Wang, Z., & Sun, Y. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.048 · Cited by 6

Trustworthy AI Using Confidential Federated Learning

Guo, J., Pietzuch, P. R., Paverd, A., & Vaswani, K. (2024) · Communications of the ACM · doi:10.1145/3677390 · Cited by 6

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