Romoa: Robust Model Aggregation for the Resistance of Federated Learning to Model Poisoning Attacks

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

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Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

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Ensemble Distillation for Robust Model Fusion in Federated LearningPreprint

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Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural NetworksPreprint

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Lookahead Optimizer: k steps forward, 1 step backPreprint

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Secure Single-Server Aggregation with (Poly)Logarithmic Overhead

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Attack of the tails: Yes, you really can backdoor federated learning

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SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead

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Asynchronous Stochastic Gradient Descent with Delay CompensationPreprint

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The Limitations of Federated Learning in Sybil Settings

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A <scp>uror</scp>: defending against poisoning attacks in collaborative deep learning systems

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Poisoning Attack in Federated Learning using Generative Adversarial Nets

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Analyzing Federated Learning through an Adversarial Lens

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FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated LearningPreprint

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When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning AttacksPreprint

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Data Poisoning Attacks to Deep Learning Based Recommender Systems

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The Privacy Implications of Cyber Security Systems

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Robust Federated Learning: The Case of Affine Distribution ShiftsPreprint

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Defending Against Saddle Point Attack in Byzantine-Robust Distributed LearningPreprint

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Data Poisoning Attacks to Local Differential Privacy ProtocolsPreprint

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Robust Federated Learning via Collaborative Machine Teaching

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Verifying Individual Fairness in Machine Learning ModelsPreprint

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Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness

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Towards Realistic Byzantine-Robust Federated Learning

Portnoy, A., & Hendler, D. (2020) · Cited by 15 · see citing works

Model aggregation techniques in federated learning: A comprehensive survey

Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.008 · Cited by 473 · see citing works

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 · see citing works

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 97 · see citing works

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

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FLARE

Wang, N., Xiao, Y., Chen, Y., Yang, H., Lou, W., & Hou, Y. T. (2022) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3488932.3517395 · Cited by 83 · see citing works

A Detailed Survey on Federated Learning Attacks and Defenses

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SafeFL: MPC-friendly Framework for Private and Robust Federated Learning

Gehlhar, T., Marx, F., Schneider, T. H., Suresh, A., Wehrle, T., & Yalame, H. (2023) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw59333.2023.00012 · Cited by 60 · see citing works

Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers

Arbaoui, M., Brahmia, M., Rahmoun, A., & Zghal, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678182 · Cited by 49 · see citing works

Long-Short History of Gradients Is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

Gupta, A., Luo, T., Ngo, M. V., & Das, S. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17143-7_22 · Cited by 37 · see citing works

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

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Towards robust and privacy-preserving federated learning in edge computing

Zhou, H., Zheng, Y., & Jia, X. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110321 · Cited by 32 · see citing works

Sine: Similarity is Not Enough for Mitigating Local Model Poisoning Attacks in Federated Learning

Kasyap, H., & Tripathy, S. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3353317 · Cited by 30 · see citing works

Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data

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An adaptive robust defending algorithm against backdoor attacks in federated learning

Wang, Y., Zhai, D.-H., He, Y., & Xia, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.026 · Cited by 25 · see citing works

Secure Model Aggregation Against Poisoning Attacks for Cross-Silo Federated Learning With Robustness and Fairness

Mao, Y., Ye, Z., Yuan, X., & Zhong, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3416042 · Cited by 25 · see citing works

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 · see citing works

Turning Federated Learning Systems Into Covert Channels

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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 20 · see citing works

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 17 · see citing works

PBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0

Li, W., Fan, K., Yang, K., Yang, Y., & Li, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3315226 · Cited by 15 · see citing works

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 16 · see citing works

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 · see citing works

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 12 · see citing works

A Secure Object Detection Technique for Intelligent Transportation Systems

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FLARE: Defending Federated Learning Against Model Poisoning Attacks via Latent Space Representations

Wang, N., Zhang, C., Xiao, Y., Chen, Y., Lou, W., & Hou, Y. T. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3521297 · Cited by 11 · see citing works

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 · see citing works

Tactile internet of federated things: Toward fine-grained design of FL-based architecture to meet TIoT demands

Alnajar, O., & Barnawi, A. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109712 · Cited by 8 · see citing works

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 · see citing works

Robust and privacy-preserving collaborative training: a comprehensive survey

Yang, F., Zhang, X., Guo, S., Chen, D., Gan, Y., Xiang, T., & Liu, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10797-0 · Cited by 15 · see citing works

Detection and Mitigation of Targeted Data Poisoning Attacks in Federated Learning

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Robust Federated Learning against Backdoor Attackers

Ranjan, P., Gupta, A., Corò, F., & Das, S. K. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10225922 · Cited by 5 · see citing works

Attacks and countermeasures on federated learning via historical knowledge modeling

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BaDFL: Mitigating Model Poisoning in Decentralized Federated Learning

Yuan, Y., Zhou, A., Zhang, X., Zou, Y., Shi, Y., & Yu, D. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3603683 · Cited by 3 · see citing works

“Malicious or Benign?”: Enhancing the Contribution of Model Updates in Byzantine-Robust Heterogeneous Federated Learning

Zhang, Y., Wang, L., Li, M., Gai, K., & Wang, J. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3654756 · Cited by 3 · see citing works

Two-Dimensional Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks

Gu, K., Zhao, W., Tan, J., Yang, Z., Li, X., & Jia, W. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3656703 · Cited by 3 · see citing works

Detecting Data Poisoning Attacks using Federated Learning with Deep Neural Networks: An Empirical Study

Alsuwat, H. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141170 · Cited by 3 · see citing works

Explore Patterns to Detect Sybil Attack during Federated Learning in Mobile Digital Twin Network

Zhang, W., Yu, C., Meng, Z., Shen, S., & Zhang, K. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622975 · Cited by 2 · see citing works

Robust Federated Learning Against Targeted Attackers Using Model Updates Correlation

Ranjan, P., Gupta, A., & Das, S. K. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-58923-2_4 · Cited by 2 · see citing works

Federated Learning Poison Attack Detection Scheme Based on Gradient Similarity

Zhao, Q., Zhang, Y., Jing, Z., Zhou, Y., & Xin, Z. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80419-9_2 · Cited by 1 · see citing works

A new federated learning system with attention-aware aggregation method for intrusion detection systems

Vu, L., Tran, T. P., & Nguyen, Q. U. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-329032-9.00014-2 · Cited by 1 · see citing works

On the Conflict Between Robustness and Learning in Collaborative Machine Learning

Raynal, M., & Troncoso, C. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00249 · Cited by 1 · see citing works

FLSwitch: Towards Secure and Fast Model Aggregation for Federated Deep Learning with a Learning State-Aware Switch

Mao, Y., Dang, Z., Lin, Y., Zhang, T., Zhang, Y., Hua, J., & Zhong, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33488-7_18

A Survey of Federated Learning: Review, Attacks, Defenses

Yao, Z., Cheng, J., Fu, C., & Huang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4387-2_13

Multi-index federated aggregation algorithm based on trusted verification

Bao, Z., Wang, M., Bai, W., & Zhang, W. (2024) · CCF Transactions on High Performance Computing · doi:10.1007/s42514-024-00199-7

Defending Federated Learning Against Compound Targeted Attacks

Boschiero, L., Conti, M., Corò, F., & Meneghello, M. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00112

Enhancing Privacy and Robustness in Federated Learning with Local Data Distribution Invariance and Byzantine-Resilient Aggregation

Dolo, B., Loukil, F., Boukadi, K., & Salamatian, K. (2025) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre66568.2025.00053

An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications

Mia, M. J., & Amini, M. H. (2025) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486115.044

FedDLFA: A Robust Defense Mechanism Against Label-Flipping Attacks in Federated Learning

Hu, S., Wang, C., & Li, Y. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343507

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

Shejwalkar, V., & Houmansadr, A. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24498 · Cited by 615 · see citing works

A novel aggregation method to promote safety security for poisoning attacks in Federated Learning

Barros, P. H., & Ramos, H. S. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001680 · Cited by 8 · see citing works

FedRRA: Reputation-Aware Robust Federated Learning against Poisoning Attacks

Yi, L., Shi, X., Wang, W., Wang, G., & Liu, X. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191556 · Cited by 14 · see citing works

Robust and Efficient Federated Learning Algorithms against Adaptive Model Poisoning Attacks

Yang, H. (2024) · Open Access at Essex (University of Essex)

Secure Model Aggregation Against Poisoning Attacks for Cross-Silo Federated Learning With Robustness and Fairness

Mao, Y., Ye, Z., Yuan, X., & Zhong, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3416042 · Cited by 25 · see citing works