FL-Defender: Combating targeted attacks in federated learning

Deep Residual Learning for Image Recognition

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Learning Multiple Layers of Features from Tiny Images

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Communication-Efficient Learning of Deep Networks from Decentralized Data

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Principal component analysis

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Density-Based Clustering Based on Hierarchical Density Estimates

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Towards Federated Learning at Scale: System Design

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Artificial intelligence and statistics

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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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How to backdoor federated learning

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works

Poisoning Attacks against Support Vector Machines

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IEEE Symposium on Security and Privacy

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Federated Learning for Mobile Keyboard Prediction

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130 · see citing works

BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

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In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

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Object Recognition with Gradient-Based Learning

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Data Poisoning Attacks Against Federated Learning Systems

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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

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Predicting Parameters in Deep Learning

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Estimating a Dirichlet Distribution

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Robust regression using repeated medians

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Federated Optimization:Distributed Optimization Beyond the DatacenterPreprint

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

Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works

Can You Really Backdoor Federated Learning?

Sun, Z., Kairouz, P., Suresh, A. T., & McMahan, H. B. (2019) · arXiv · doi:10.48550/arxiv.1911.07963 · Cited by 348 · see citing works

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

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Exploiting machine learning to subvert your spam filter

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Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks

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Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy

We, Z., & Li, X. (2021) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3065522 · Cited by 210 · see citing works

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 · see citing works

Achieving security and privacy in federated learning systems: Survey, research challenges and future directions

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Shape, Contour and Grouping in Computer Vision

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Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions

We, Z., & Li, X. (2021) · Structural Health Monitoring · doi:10.1177/14759217211029201 · Cited by 150 · see citing works

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentPreprint

Chen, Y., Su, L., & Xu, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05491 · Cited by 136 · see citing works

Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge TransferPreprint

Chang, H., Shejwalkar, V., Shokri, R., & Houmansadr, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11279 · Cited by 131 · see citing works

FLGUARD: Secure and Private Federated Learning

Nguyen, T. D., Rieger, P., Yalame, H., Möllering, H., Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., Sadeghi, A., Schneider, T. H., et al. (2021) · TUbilio (Technical University of Darmstadt) · Cited by 55 · see citing works

Enhanced Security and Privacy via Fragmented Federated Learning

Jebreel, N., Domingo‐Ferrer, J., Blanco-Justicia, A., & Sánchez, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212627 · Cited by 46 · see citing works

Auto-weighted Robust Federated Learning with Corrupted Data Sources

Li, S., Ngai, E. C. ‐., Ye, F., & Voigt, T. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3517821 · Cited by 34 · see citing works

Defending against the Label-flipping Attack in Federated LearningPreprint

Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.01982 · Cited by 26 · see citing works

Efficient Detection of Byzantine Attacks in Federated Learning Using Last Layer Biases

Jebreel, N., Blanco-Justicia, A., Sánchez, D., & Domingo‐Ferrer, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57524-3_13 · Cited by 16 · see citing works

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 · see citing works

The Impact of Adversarial Attacks on Federated Learning: A Survey

Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322785 · Cited by 191 · see citing works

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 · see citing works

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions

Liu, Z., Jiang, Y., Shen, J., Peng, M., Lam, K., Yuan, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3679014 · Cited by 148 · see citing works

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 97 · see citing works

Personalized federated learning-based intrusion detection system: Poisoning attack and defense

Thein, T. T., Shiraishi, Y., & Morii, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.005 · Cited by 94 · see citing works

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 116 · see citing works

A Credible and Fair Federated Learning Framework Based on Blockchain

Chen, L., Zhao, D., Tao, L., Wang, K., Qiao, S., Zeng, X., & Tan, C. W. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3355362 · Cited by 68 · see citing works

A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy

Manzoor, H. U., Shabbir, A., Chen, A., Flynn, D., & Zoha, A. (2024) · Future Internet · doi:10.3390/fi16100374 · Cited by 58 · see citing works

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 · see citing works

Toward Efficient and Certified Recovery From Poisoning Attacks in Federated Learning

Jiang, Y., Shen, J., Liu, Z., Tan, C. W., & Lam, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3533907 · Cited by 48 · see citing works

SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments

Cao, Y., Zhang, J., Zhao, Y., Su, P., & Huang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122410 · Cited by 43 · see citing works

FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks

Kabir, E., Song, Z., Rashid, M. R. U., & Mehnaz, S. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00141 · Cited by 41 · see citing works

A survey on privacy-preserving federated learning against poisoning attacks

Feng, X., & Cheng, W. (2024) · Cluster Computing · doi:10.1007/s10586-024-04629-7 · Cited by 33 · see citing works

DPFLA: Defending Private Federated Learning Against Poisoning Attacks

Feng, X., Cheng, W., Cao, C., Wang, L., & Sheng, V. S. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3376255 · Cited by 29 · see citing works

Securing Federated Diffusion Model With Dynamic Quantization for Generative AI Services in Multiple-Access Artificial Intelligence of Things

He, J., Lai, B., Kang, J., Du, H., Nie, J., Zhang, T., Yuan, Y., Zhang, W., Niyato, D. T., & Jamalipour, A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3420696 · Cited by 27 · see citing works

RoPE: Defending against backdoor attacks in federated learning systems

Wang, Y., Zhai, D., & Xia, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111660 · Cited by 26 · see citing works

Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack

Rezaie, H., Taheri, R., Jordanov, I., & Shojafar, M. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09963-8 · Cited by 25 · see citing works

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective

Kumar, K. N., Mohan, C. K., Cenkeramaddi, L. R., & Awasthi, N. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103024 · Cited by 21 · see citing works

ANODYNE: Mitigating backdoor attacks in federated learning

Gu, Z., Shi, J., & Yang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125359 · Cited by 18 · see citing works

HidAttack: An Effective and Undetectable Model Poisoning Attack to Federated Recommenders

Ali, W., Umer, K., Zhou, X., & Shao, J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3522763 · Cited by 19 · see citing works

SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks

Soomro, I. A., Rehman, H. U., Hussain, S. J., Iqbal, A., Khalid, W., & Yu, H. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3647642 · Cited by 19 · see citing works

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 16 · see citing works

Federated Learning Minimal Model Replacement Attack Using Optimal Transport: An Attacker Perspective

Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3516555 · Cited by 14 · see citing works

FLMAAcBD: Defending against backdoors in Federated Learning via Model Anomalous Activation Behavior Detection

Cai, H., Wang, J., Gao, L., & Li, F. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111511 · Cited by 13 · see citing works

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 · see citing works

Enhanced Adversarial Attack Resilience in Energy Networks through Energy and Privacy Aware Federated LearningPreprint

Manzoor, H. U., Arshad, K., Assaleh, K. T., & Zoha, A. (2024) · doi:10.36227/techrxiv.171468119.91309905/v1 · Cited by 13 · see citing works

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 · see citing works

MemberShield: A framework for federated learning with membership privacy

Ahmed, F., Sánchez, D., Haddi, Z., & Domingo‐Ferrer, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106768 · Cited by 12 · see citing works

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 · see citing works

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 · see citing works

FedCleanse: Cleanse the backdoor attacks in federated learning system

Huang, S., Li, Y., Chen, C., Shi, L., Cai, W., & Gao, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114494 · Cited by 8 · see citing works

FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning

Li, H., Rieger, P., Zeitouni, S., Picek, S., & Sadeghi, A. (2023) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl60245.2023.00046 · Cited by 8 · see citing works

Optimal Transport Barycentric Aggregation for Byzantine-Resilient Federated Learning

Kumar, K. N., Chalamala, S. R., Singh, A. K., & Mohan, C. K. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3604177 · Cited by 8 · see citing works

FMDL: Federated Mutual Distillation Learning for Defending Backdoor Attacks

Sun, H., Zhu, W., Sun, Z., Cao, M., & Liu, W. (2023) · Electronics · doi:10.3390/electronics12234838 · Cited by 7 · see citing works

Preventing the Popular Item Embedding Based Attack in Federated Recommendations

Zhang, J., Li, H., Rong, D., Zhao, Y. L., Chen, K., & Shou, L. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00173 · Cited by 7 · see citing works

FedSyPo: Detection of Sybil-Poisoning attack in federated leaning on non-IID data for 6G-based IoT-Edge Network

Ashwini, P., & Rout, R. R. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111656 · Cited by 6 · see citing works

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning

Kumar, K. N., Mohan, C. K., & Machiry, A. (2024) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3626232.3653268 · Cited by 6 · see citing works

Can We Trust the Similarity Measurement in Federated Learning?

Wang, Z., Hu, Q., Zou, X., Hu, P., & Cheng, X. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3516567 · Cited by 6 · see citing works

Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning

Yang, J., Zheng, J., Wang, H., Li, J., Sun, H., Han, W., Jiang, N., & Tan, Y. (2023) · Sensors · doi:10.3390/s23031052 · Cited by 6 · see citing works

Consumption prediction with privacy concern: Application and evaluation of Federated Learning

Wang, Y., Zobiri, F., Mustafa, M., Nightingale, J., & Deconinck, G. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101248 · Cited by 6 · see citing works

A robust federated learning algorithm for partially trusted environments

Li, Y., Liu, T., Ling, H., Du, W., & Ren, X. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104161 · Cited by 5 · see citing works

A Lightweight Robust Training Method for Defending Model Poisoning Attacks in Federated Learning Assisted UAV Networks

Chen, L., Zhai, W., Bu, X., Sun, M., & Zhu, C. (2025) · Drones · doi:10.3390/drones9080528 · Cited by 5 · see citing works

Privacy-Preserving Federated Learning With Improved Personalization and Poison Rectification of Client Models

Cao, Y., Zhang, J., Zhao, Y., Shen, H., & Huang, H. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3463532 · Cited by 5 · see citing works

AntidoteFL: Enhancing defense against poisoning attacks in federated learning

Liu, Y., Zhang, H., Wang, M., Xie, Q., & Sun, Z. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111427 · Cited by 5 · see citing works

Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning

Jiang, Y., Chen, S., & Bao, X. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3341836 · Cited by 5 · see citing works

Byzantine-robust federated learning with ensemble incentive mechanism

Shi-hai, Z., Pu, J., Fu, X., Liu, L., & Dai, F. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.017 · Cited by 5 · see citing works

NP-FedKGC: a neighbor prediction-enhanced federated knowledge graph completion model

Liu, S., Li, W., Song, X., & Gong, K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06201-7 · Cited by 4 · see citing works

Systemization of Knowledge: Robust Deep Learning using Hardware-software co-design in Centralized and Federated Settings

Zhang, R., Hussain, S., Chen, H., Javaheripi, M., & Koushanfar, F. (2023) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3616868 · Cited by 4 · see citing works

A Secure and Trustworthy Federated Learning Platform as a Service Model for Stroke Management in European Clinical Centers

Santos, D. R., Aillet, A. S., Boiano, A., Gennaro, M. D., Taiello, R., Milasheuski, U., Giusti, L., Costa, M., Brambilla, M., Cacace, P., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3641035 · Cited by 3 · see citing works

A Review on Fuzzy Cognitive Mapping: Recent Advances and Algorithms

Nápoles, G., Jastrzębska, A., Grau, I., Salgueiro, Y., & León, M. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10010022 · 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

Toward Adversarial Robustness in MADRL-Based EV Charging Scheduling: A Tailored Multicriteria Adversarial Attack and Defense Framework

Dai, Z., Tan, M., Yang, Y., Liu, X., Su, Y., & Li, K. (2026) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2026.3664019 · Cited by 3 · see citing works

A Secure and Fair Client Selection Based on DDPG for Federated Learning

Wan, T., Feng, S., Liao, W., Jiang, N., & Zhou, J. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/2314019 · Cited by 3 · see citing works

Dual-Layered Model Protection Scheme Against Backdoor Attacks in Fog Computing-Based Federated Learning

Gu, K., Zuo, Y., Tan, J., Yin, B., Yang, Z., & Li, X. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3509983 · Cited by 2 · see citing works

Model Poisoning Detection and Dismissal Safeguarding Federated Learning Against Malicious Attacks

Rahman, M. M., Debnath, A. C., & Giordano, S. (2024) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad62243.2024.10942888 · Cited by 2 · see citing works

Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach

Erdöl, E. S., Erdöl, H., Üstübioğlu, B., Ulutaş, G., & Symeonidis, I. (2025) · Applied Sciences · doi:10.3390/app15116007 · Cited by 2 · see citing works

Adversarial feature generation for ML-based intrusion detection in the petrochemical industry

Naqvi, S. S. A., Zhou, C., Xu, P., Li, Y., Jiashu, J., & Uzair, M. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104215 · Cited by 2 · see citing works

Breaking the Illusion: A Critical Study of Backdoor Defense in Federated Learning With Non-IID Data

Ye, P., Li, Y., He, K., Li, Q., Qin, T., Wang, X., Yang, K., Zhang, C., & Chen, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3643155 · Cited by 2 · see citing works

Node Compromising Detection to Mitigate Poisoning Attacks in IoT Networks

Vuseghesa, F. K., Messai, M., & Bentayeb, F. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592570 · Cited by 2 · see citing works

SecDefender: Detecting low-quality models in multidomain federated learning systems

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Defending against diverse attacks in federated learning through consensus-based bi-level optimization

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Toward Model-Contrastive Federated Learning With Lightweight Privacy Preservation and Poisoning Attack Detection

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Defending against Poisoning Attacks in Federated Learning from a Spatial-temporal Perspective

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data

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Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

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Resisting against targeted poisoning attacks in lightweight privacy preserving federated learning

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Vulnerabilities of label and model protection in vertical federated learning based on simulation models

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Untargeted poisoning attack based on fake clients and its defense in federated learning

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Co-distillation-based defense framework for federated knowledge graph embedding against poisoning attacks

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Trustworthy Distributed Load Forecasting in Resource‐Limited Smart Grids and Buildings via Random Layer Aggregation

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Frequency-Domain Signatures for Proactive Defense Against Model Poisoning Attacks in Federated Learning

Hu, F., Zhang, A., Li, M., & Wang, C. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3689720 · Cited by 1 · see citing works

Defending against Adversarial Attacks in Federated Learning on Metric Learning Model

Gu, Z., Shi, J., Yang, Y., & He, L. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00047 · Cited by 1 · see citing works

WeiDetect: Weibull distribution-based defense against poisoning attacks in federated learning for network intrusion detection systems

Sameera, K. M., Vinod, P., Rocha, A. D. R., A., R. R. K., & Conti, M. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104275 · Cited by 1 · see citing works

Detecting Low-Quality Models in Multidomain Federated Learning SystemsPreprint

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SL-Defender: Decentralized Defense for Adversarial Robustness in Federated Learning

Justine, C., Manamohan, S., & Jiji, C. V. (2023) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/iccst59048.2023.10474244

Unfair Trojan: Targeted Backdoor Attacks Against Model Fairness

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DLShield: A Defense Approach Against Dirty Label Attacks in Heterogeneous Federated Learning

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FEFL: A Fair & Efficient Defense Against Poisoning Attacks via Discrete Update Space

Zhao, Y., Zhang, J., Cao, Y., & Ye, T. (2024) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp62589.2024.10809244

FL-ProtectX: Combating Label flipping in complex multi-agent environments

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Federated Learning: An Overview of Attacks and Defense Methods

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A distributed bijection-backdoor-based adversarial examples defense method in federated learning

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Robust clustering federated learning with trusted anchor clients

Zhang, M., Li, Y., Wei, F., Wang, B., & Zhang, Y. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104210

Poisoning Attack on Federated Learning with Non-IID Data: A Historical-Global-Model-Based Approach

Sun, Y., Wang, X., Zhang, Z., Ming, Y., He, Y., & Wu, X. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-94458-1_12

AP-PPFL: an anti-poisoning privacy-preserving federated learning method

Li, Y., Chen, F., Chang, C., Guo, Y., Sun, H., & Hao, Y. (2026) · Cybersecurity · doi:10.1186/s42400-026-00583-6

Enhancing Security and Privacy in Multi-Server Federated Learning

Zhao, X., Bu, Y., Fan, K., & Li, H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3627244

RS-ZKP

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Class-Granular Attacks and Robust Defense in Federated Learning

Ahmed, M. F., Zhu, Z., & Wang, Z. (2025) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud66326.2025.00019

Sybil-aware adaptive defence framework for robust federated learning

Khedekar, D., Mahapatra, T., & Rajput, A. S. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102157

A Defense Against Label Flipping Attacks in Federated Learning Using Output Layer Gradients

Yu, M., & Gong, B. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9849-3_36

Secure and Robust Federated Learning Under Dual-Server Architecture in Internet of Things

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Exploiting Parasitic Dependency for Free-Rider Elimination in Blockchain-Based Federated Learning

Duan, D., Zheng, X., Zheng, Y., Mu, C., Wang, R., & Yan, K. (2025) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc66453.2025.11304677

Enhancing Federated Learning Security: Cluster-Based Strategies to Counter GAN-Poisoned Attacks

Nowroozi, E., Habibi, Y., Mughal, A. B., Saedi, M., & Jafari, M. (2026) · Springer eBooks · doi:10.1007/978-3-031-99447-0_16

Poisoning Attacks in Heterogeneous Federated Learning Networks

Sameera, K. M., Mukati, K., Kavya, M. L., Preethi, S. L., Rughani, P. H., Radhamani, G., Rehiman, K. A. R., & Vinod, P. (2026) · International Conference on Advances in Intelligent Computing and Applications (AICAPS) · doi:10.1109/aicaps68631.2026.11453269

Navigating threats in federated recommender systems: a survey of attack models and defense strategies

Ali, W., Altulyan, M. S., Ihnaini, B., Zhou, X., & Shao, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11579-6

Neuron-level defense against backdoor attacks in federated learning

Luo, J., Zhang, X., Bai, F., Zeng, K., & Shen, T. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03114-4

RDFDS: A Federated Learning Defense Framework in IoV With Fast Distillation Synthesis and Clustering-Based Aggregation

Zhang, L., Zhang, X., Qin, H., Wang, H., & Lin, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3702768

SADFL: Spectral Anomaly Detection with adversarial learning in Federated Learning for defending malicious client attacks

Ruan, J.-H., Zhang, Z.-L., Liu, W.-A., Ruan, Y.-P., & Xiong, Q. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104973

BRPPFL: A Byzantine-Robust and Privacy-Preserving Framework for Cloud–Edge Federated Learning

Zhang, L., Qiancheng, Y., Wuwei, Y., & He, L. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112498

Label-Flip Attack Detection via Trust-Weighted Aggregation in Federated Learning for Underground Mine Security

Rahman, M. S., Madria, S., & Frimpong, S. (2026) · ACM International Conference on Distributed and Event-Based Systems (DEBS) · doi:10.1145/3809481.3812617

A comprehensive survey on communication optimization and privacy preservation in federated learning

Kunjumon, A., Sarath, G., & Nair, J. J. (2026) · Discover Computing · doi:10.1007/s10791-026-10272-0

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 116 · see citing works

Defending against the Label-flipping Attack in Federated LearningPreprint

Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.01982 · Cited by 26 · see citing works

LFighter: Defending against the Label-flipping Attack in Federated Learning (Extended Abstract)Preprint

Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5528538 · Cited by 1 · see citing works

FedDefender: Backdoor Attack Defense in Federated Learning

Gill, W., Anwar, A., & Gulzar, M. A. (2023) · doi:10.1145/3617574.3617858 · Cited by 12 · see citing works

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 · see citing works