FL-Defender: Combating targeted attacks in federated learning

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215

Principal component analysis

Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792

Density-Based Clustering Based on Hierarchical Density Estimates

Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37456-2_14 · Cited by 2,680

Artificial intelligence and statistics

Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,367

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

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

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

Poisoning Attacks against Support Vector Machines

Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331

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 209

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

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 182

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

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

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 104

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

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

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 77

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

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

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

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

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

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 34

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

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 29

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

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

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 24

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 20

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 22

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

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

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 15

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

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

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

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

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 13

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

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

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 7

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 5

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

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

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

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

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

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

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 4

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

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

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

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

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

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

Sameera, K. M., Sgueglia, A., Vinod, P., A., R. R. K., Visaggio, C. A., Sorbo, A. D., & Conti, M. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107587 · Cited by 2

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

Trillos, N. G., Akash, A. K., Li, S., Riedl, K., & Zhu, Y. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0235 · Cited by 2

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

Zhang, H., Yu, Z., Wang, G., Xu, F., Zhang, Y., Hu, C., Wang, X., & Yu, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3620529 · Cited by 2

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

Gu, Z., Shi, J., Yang, Y., & He, L. (2023) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds60354.2023.00013 · Cited by 2

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

Zhang, J., Zhang, H., Wang, G., & Dong, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71467-2_6 · Cited by 1

Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

Cai, L. Z., Gu, K., & Lei, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061377 · Cited by 2

Resisting against targeted poisoning attacks in lightweight privacy preserving federated learning

Zhang, H., Xie, H., & Lv, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07829-z · Cited by 2

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

Zhao, X., Ke, Z., Yu, H., Yi, L., Chen, H., Hui, X., Li, Z., Liu, S., & Shi, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131839 · Cited by 1

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

Wang, C., Xu, K., He, A., Sun, Z., Pan, J., & Ren, Y. (2025) · Cybersecurity · doi:10.1186/s42400-025-00389-y · Cited by 1

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

Lu, Y., Chen, J., & Qin, J. (2025) · Computer Standards & Interfaces · doi:10.1016/j.csi.2025.104113 · Cited by 1

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

Manzoor, H. U., Shabbir, A., Rais, R. N. B., Hussain, S., & Zoha, A. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/8810907 · Cited by 1

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

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

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

Detecting Low-Quality Models in Multidomain Federated Learning SystemsPreprint

Sameera, K. M., Sgueglia, A., Vinod, P., Rehiman, K. A. R., Visaggio, C. A., & Sorbo, A. D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4604411

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

Furth, N., Khreishah, A. A., Liu, G., Phan, N., & Jararweh, Y. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-58923-2_5

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

Sameera, K. M., Abhinav, M., Amal, P. P., Abhiram, T., Abishek, R. K., Amal, T., Anaina, P., Vinod, P., A., R. R. K., & Conti, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-80408-3_9

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

Zamani, M. A., Amiri, F., Jamshidi, P., Hosseini, S. M., & Yazdani, N. (2024) · International Symposium on Telecommunications · doi:10.1109/ist64061.2024.10843483

Federated Learning: An Overview of Attacks and Defense Methods

Sameera, K. M., Arikkat, D. R., Vinod, P., A., R. R. K., Aneez, A., & Conti, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-83157-7_14

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

Li, Y., Guo, Y., Chen, F., Wang, Y., Chen, Q., & Hu, Y. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02750-6

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

Prakash, P., Sanil, F. K., Shaji, J. T., Palani, S., & Sudharson, S. (2025) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.394815

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

Liu, S., Ren, J., Meng, R., Yan, X., Miao, Y., Liu, Z., Peng, J., Zou, Y., Wang, Z., Li, H., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3647441

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

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