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

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

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

NSPFL: A Novel Secure and Privacy-Preserving Federated Learning With Data Integrity Auditing

Zhang, Z., & Li, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3379852 · Cited by 22

An efficient privacy-preserving and verifiable scheme for federated learning

Yang, X. S., Ma, M., & Tang, X. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.002 · Cited by 22

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

SHIELD - Secure Aggregation Against Poisoning in Hierarchical Federated Learning

Siriwardhana, Y., Porambage, P., Liyanage, M., Marchal, S., & Ylianttila, M. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3472869 · Cited by 20

Poisoning Attacks against Federated Learning in Load Forecasting of Smart Energy

Qureshi, N. B. S., Kim, D., Lee, J., & Lee, E. (2022) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms54207.2022.9789884 · Cited by 20

LR-BA: Backdoor attack against vertical federated learning using local latent representations

Gu, Y., & Bai, Y. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103193 · Cited by 22

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

Zhang, H., Yao, Z., Zhang, L. Y., Hu, S., Chen, C., Liew, A. W., & Li, Z. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/508 · Cited by 26

Privacy-Preserving Federated Learning Approach for Distributed Malware Attacks With Intermittent Clients and Image Representation

Ullah, F., Srivastava, G., Ullah, S., & Mostarda, L. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3342644 · Cited by 20

BIT-FL: Blockchain-Enabled Incentivized and Secure Federated Learning Framework

Ying, C., Xia, F., Wei, D. S. L., Yu, X., Xu, Y., Zhang, W., Jiang, X., Jin, H., Luo, Y., Zhang, T., et al. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3477616 · Cited by 20

Advances in Appfl: a Comprehensive and Extensible Federated Learning Framework

Li, Z., He, S., Yang, Z., Ryu, M., Kim, K., & Madduri, R. (2025) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid64434.2025.00031 · Cited by 20

FLRAM: Robust Aggregation Technique for Defense against Byzantine Poisoning Attacks in Federated Learning

Chen, H., Chen, X., Peng, L., & Ma, R. (2023) · Electronics · doi:10.3390/electronics12214463 · Cited by 21

Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing

Moore, E., Imteaj, A., Hossain, M. Z., Rezapour, S., & Amini, M. H. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3539258 · Cited by 19

DistFL

Liu, B., Cai, Y., Zhang, Z., Li, Y., Wang, L., Li, D., Guo, Y., & Chen, X. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3494966 · Cited by 31

Security and privacy in federated learning: A survey

Kandati, D. R., & Anusha, S. (2023) · Trends in Computer Science and Information Technology · doi:10.17352/tcsit.000066 · Cited by 19

Selective Trimmed Average: A Resilient Federated Learning Algorithm With Deterministic Guarantees on the Optimality Approximation

Kaheni, M., Lippi, M., Gasparri, A., & Franceschelli, M. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3350387 · Cited by 21

Supporting Trustworthy AI Through Machine Unlearning

Hine, E., Novelli, C., Taddeo, M., & Floridi, L. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00500-5 · Cited by 24

A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study

Almutairi, S., & Barnawi, A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103295 · Cited by 21

A Privacy-Preserving Federated Learning Framework for IoT Environment Based on Secure Multi-party Computation

Geng, T., Liu, J., & Huang, C. (2024) · IEEE Annual Congress on Artificial Intelligence of Things (AIoT) · doi:10.1109/aiot63253.2024.00032 · Cited by 18

A New Path to Integrated Learning and Communication (ILAC): Large AI Models Leveraging Hyperdimensional Computing

Xu, W., Yang, Z., Ng, D. W. K., Schober, R., Poor, H. V., Zhang, Z., & You, X. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2026.3662396 · Cited by 20

Detecting Network Attacks using Federated Learning for IoT Devices

Shahid, O., Mothukuri, V., Pouriyeh, S., Parizi, R. M., & Shahriar, H. (2021) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp52444.2021.9651915 · Cited by 22

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation

Park, S. J., & Ye, J. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218783 · Cited by 18

Turning Federated Learning Systems Into Covert Channels

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2022) · IEEE Access · doi:10.1109/access.2022.3229124 · Cited by 35

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

Defending against model poisoning attack in federated learning: A variance-minimization approach

Xu, H., & Shu, T. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103744 · Cited by 20

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

Evaluating Security and Robustness for Split Federated Learning Against Poisoning Attacks

Wu, X., Yuan, H., Li, X., Ni, J., & Lu, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3490861 · Cited by 17

ARFED: Attack-Resistant Federated averaging based on outlier elimination

Isik-Polat, E., Polat, G., & Koçyiğit, A. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.12.003 · Cited by 25

HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis

Duan, S., Liu, C., Han, P., Jin, X., Zhang, X., He, T., Pan, H., & Xiang, X. (2022) · Entropy · doi:10.3390/e25010088 · Cited by 17

Visual Object Detection for Privacy-Preserving Federated Learning

Zhang, J., Zhou, J., Guo, J., & Sun, X. (2023) · IEEE Access · doi:10.1109/access.2023.3263533 · Cited by 19

Attacking-Distance-Aware Attack: Semi-targeted Model Poisoning on Federated Learning

Sun, Y., Ochiai, H., & Sakuma, J. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3280155 · Cited by 19

Secure Split Learning Against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks

Mao, Y., Xin, Z., Li, Z., Hong, J., Yang, Q., & Zhong, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51482-1_2 · Cited by 23

Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting

Dong, Y., Wang, Y., da Gama, M. B., Mustafa, M., Deconinck, G., & Huang, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3362587 · Cited by 20

SF-CABD: Secure Byzantine fault tolerance federated learning on Non-IID data

Lin, X., Li, Y., Xie, X., Ding, Y., Wu, X., & Ge, C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111851 · 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

FedMP: A multi-pronged defense algorithm against Byzantine poisoning attacks in federated learning

Zhao, K., Wang, L., Yu, F., Zeng, B., & Pang, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110990 · Cited by 19

Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning

Tian, Y., Zhang, W., Simpson, A. C., Liu, Y., & Jiang, Z. L. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab192 · Cited by 16

Cryptanalysis and Improvement of DeepPAR: Privacy-Preserving and Asynchronous Deep Learning for Industrial IoT

Chen, Y., He, S., Wang, B., Duan, P., Zhang, B., Hong, Z., & Ping, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3181665 · Cited by 16

Siren+: Robust Federated Learning With Proactive Alarming and Differential Privacy

Guo, H., Wang, H., Song, T., Yang, H., Ma, R., Jin, X., Xue, Z., & Guan, H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3362534 · Cited by 17

FlexibleFL: Mitigating poisoning attacks with contributions in cloud-edge federated learning systems

Zhao, Y., Cao, Y., Zhang, J., Huang, H., & Liu, Y. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120350 · Cited by 18

Using Third-Party Auditor to Help Federated Learning: An Efficient Byzantine-Robust Federated Learning

Zhang, Z., Wu, L., He, D., Li, J., Lu, N., & Wei, X. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3379440 · Cited by 16

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Wu, J., Jin, J., & Wu, C. (2024) · Mathematics · doi:10.3390/math12060901 · Cited by 15

A comprehensive review of current trends, challenges, and opportunities in text data privacy

Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 19

Hardware Security in the Connected World

Chatterjee, D., Maitra, S., Mishra, N., Shukla, S., & Mukhopadhyay, D. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70034 · Cited by 15

Reveal Your Images: Gradient Leakage Attack Against Unbiased Sampling-Based Secure Aggregation

Yang, Y., Ma, Z., Xiao, B., Liu, Y., Li, T., & Zhang, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3271432 · Cited by 16

Vertical Federated Unlearning on the Logistic Regression Model

Deng, Z., Han, Z., Ma, C., Ding, M., Yuan, L., Ge, C., & Liu, Z. (2023) · Electronics · doi:10.3390/electronics12143182 · Cited by 15

Data Poisoning Attacks With Hybrid Particle Swarm Optimization Algorithms Against Federated Learning in Connected and Autonomous Vehicles

Cui, C., Du, H., Jia, Z., Zhang, X., He, Y., & Yang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3337638 · Cited by 16

T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things

Cao, Y., Liu, D., Zhang, S., Wu, T., Xue, F., & Tang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123006 · Cited by 15

Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning

Sabuhi, M., Musı́lek, P., & Bezemer, C. (2024) · Future Internet · doi:10.3390/fi16030070 · Cited by 17

Partner in Crime: Boosting Targeted Poisoning Attacks Against Federated Learning

Sun, S., Sugrim, S., Stavrou, A., & Wang, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3555429 · Cited by 15

Graph-regularized federated learning with shareable side information

Zhang, Y., Wei, S., Liu, S., Wang, Y., Xu, Y., Li, Y., & Shang, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109960 · Cited by 15

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

Stodt, F., Stodt, J., & Reich, C. (2023) · Applied Sciences · doi:10.3390/app13020782 · Cited by 16

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

Wang, S., Sahay, R., & Brinton, C. G. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279348 · Cited by 15

Poisoning attacks resilient privacy-preserving federated learning scheme based on lightweight homomorphic encryption

Zhang, C., Zhang, X., Yang, X., Liu, B., Zhang, Y., & Zhou, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103131 · Cited by 17

Augmented Dual-Shuffle-based Moving Target Defense to Ensure CIA-triad in Federated Learning

Zhou, Z., Xu, C., Wang, M., Ma, T., & Yu, S. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685154 · Cited by 14

Social-Aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Ottun, A.-R. O., Mane, P. C., Yin, Z., Paul, S., Liyanage, M., Pridmore, J., Ding, A. Y., Sharma, R. R., Nurmi, P., & Flores, H. (2022) · IEEE Internet Computing · doi:10.1109/mic.2022.3219263 · Cited by 15

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16

SPEFL: Efficient Security and Privacy-Enhanced Federated Learning Against Poisoning Attacks

Shen, L., Ke, Z., Shi, J., Zhang, X., Sun, Y., Zhao, J., Wang, X., & Zhao, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3339638 · Cited by 17

Fast Adversarial Training with Smooth Convergence

Zhao, M., Zhang, L., Kong, Y., & Yin, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00435 · Cited by 16

Depriving the Survival Space of Adversaries Against Poisoned Gradients in Federated Learning

Lu, J., Hu, S., Wan, W., Li, M., Zhang, L. Y., Xue, L., & Jin, H. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3360869 · Cited by 15

FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system

Li, X., Wang, N., Yuan, S., & Guan, Z. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103936 · Cited by 14

RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System.Preprint

Liu, Y., Yuan, X., Zhao, R., Zheng, Y., & Zheng, Y. (2020) · arXiv (Cornell University) · Cited by 14

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

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2021) · arXiv (Cornell University) · Cited by 14

Data Poisoning Attack by Label Flipping on SplitFed Learning

Gajbhiye, S., Singh, P., & Gupta, S. (2023) · Communications in Computer and Information Science · doi:10.1007/978-3-031-23599-3_30 · Cited by 13

Investigating the Impact of Label-flipping Attacks against Federated Learning for Collaborative Intrusion Detection

Lavaur, L., Busnel, Y., & Autrel, F. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104462 · Cited by 14

Securing Federated Learning against Overwhelming Collusive Attackers

Ranjan, P., Gupta, A., Corò, F., & Das, S. K. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000830 · Cited by 15

Auditable Federated Learning With Byzantine Robustness

Liang, Y., Li, Y., & Shin, B. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3266019 · Cited by 13

Adaptive federated few-shot feature learning with prototype rectification

Yang, M., Chu, X. Y., Zhu, J., Xi, Y., Niu, S., & Wang, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107125 · Cited by 13

VagueGAN: A GAN-Based Data Poisoning Attack Against Federated Learning Systems

Sun, W. J., Gao, B., Xiong, K., Lu, Y., & Wang, Y. (2023) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon58729.2023.10287523 · Cited by 13

BVDFed: Byzantine-resilient and verifiable aggregation for differentially private federated learning

Gao, X., Fu, S., Liu, L., & Luo, Y. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-3142-5 · Cited by 13

FedDAA: a robust federated learning framework to protect privacy and defend against adversarial attack

Lu, S., Li, R., & Liu, W. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-023-2283-x · Cited by 13

A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Tang, Y., Zhang, Y., Niu, T., Li, Z., Zhang, Z., Chen, H., & Zhang, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.030084 · Cited by 21

Anomaly detection in log-event sequences: A federated deep learning approach and open challenges

Himler, P., Landauer, M., Skopik, F., & Wurzenberger, M. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100554 · Cited by 16

Detection and Mitigation of Label-Flipping Attacks in FL Systems With KL Divergence

Zang, L., & Li, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3424188 · Cited by 15

Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks

Zhang, Z., Fang, M., Chen, M., Li, G., Lin, X., & Liu, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421895 · Cited by 16

LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network

Sameera, K. M., Vinod, P., A., R. R. K., & Conti, M. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110768 · Cited by 13

Privacy and security of federated learning in resource-constrained Internet of Things environment: Systematic literature review

Khalaifat, W., Elmedany, W. M., & Alryalat, H. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101679 · Cited by 14

Perception Poisoning Attacks in Federated Learning

Chow, K.-H., & Liu, L. (2021) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tpsisa52974.2021.00017 · Cited by 13

FDSFL: Filtering Defense Strategies toward Targeted Poisoning Attacks in IIoT-Based Federated Learning Networking System

Xiao, X., Tang, Z., Li, Y., Song, Y., Tan, J., & Li, K. (2023) · IEEE Network · doi:10.1109/mnet.004.2200645 · Cited by 13

FedGuard: Selective Parameter Aggregation for Poisoning Attack Mitigation in Federated Learning

Chelli, M., Prigent, C., Schubotz, R., Costan, A., Antoniu, G., Cudennec, L., & Slusallek, P. (2023) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster52292.2023.00014 · Cited by 15

Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use Case

Asad, M., Otoum, S., & Shaukat, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3398213 · Cited by 15

Backdoor Attacks in Peer-to-Peer Federated Learning

Syros, G., Yar, G., Boboila, S., Nita-Rotaru, C., & Oprea, A. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3691633 · Cited by 21

Rebirth of Distributed AI—A Review of eHealth Research

Khan, M. A., & Alkaabi, N. (2021) · Sensors · doi:10.3390/s21154999 · Cited by 13

Robust Training in High Dimensions via Block Coordinate Geometric Median DescentPreprint

Acharya, A., Hashemi, A., Jain, P., Sanghavi, S., Dhillon, I. S., & Topcu, U. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08882 · Cited by 17

Toward Cleansing Backdoored Neural Networks in Federated Learning

Wu, C., Yang, X., Zhu, S., & Mitra, P. (2022) · 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00084 · Cited by 12

Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

Duy, K. D., Noh, T., Huh, S., & Lee, H. (2021) · IEEE Access · doi:10.1109/access.2021.3136889 · Cited by 15

Network-Level Adversaries in Federated Learning

Severi, G., Jagielski, M., Yar, G., Wang, Y., Oprea, A., & Nita-Rotaru, C. (2022) · IEEE Conference on Communications and Network Security · doi:10.1109/cns56114.2022.9947237 · Cited by 12

Non trust detection of decentralized federated learning based on historical gradient

Chen, Y., Li, L., & Gao, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105888 · Cited by 13

FRIMFL: A Fair and Reliable Incentive Mechanism in Federated Learning

Ahmed, A., & Choi, B. J. (2023) · Electronics · doi:10.3390/electronics12153259 · Cited by 12

Swarm Optimization-Based Federated Learning for the Cyber Resilience of Internet of Things Systems Against Adversarial Attacks

Yamany, W., Keshk, M., Moustafa, N., & Turnbull, B. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3319039 · Cited by 13

FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoT

Liu, S., Li, Z., Sun, Q., Chen, L., Zhang, X., & Li, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3341811 · Cited by 13

Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning

Wang, M., Zhu, T., Zuo, X., Ye, D., Yu, S., & Zhou, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3347899 · Cited by 12

Discriminative Feature Learning-Based Federated Lightweight Distillation Against Multiple Attacks

Chen, H., Zhao, H., Zhang, Z., & Li, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3360094 · Cited by 12

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Pirmani, A., Oldenhof, M., Peeters, L. M., Brouwer, E. D., & Moreau, Y. (2024) · JMIR Formative Research · doi:10.2196/55496 · Cited by 14

Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

Fu, X., Xiong, L., Li, F., Yang, X., & Xiong, N. N. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3439437 · Cited by 11

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction

Zhang, Z., Fang, M., Huang, J., & Liu, Y. (2024) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking62109.2024.10619763 · Cited by 12

Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

Ding, Q., Yue, X., Zhang, Q., Xiong, Z., Chang, J., & Zheng, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3485208 · Cited by 12

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