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

Personalized Decentralized Federated Learning: A Privacy-Enhanced and Byzantine-Resilient Approach

Zhang, A., Zhao, P., Lu, W., & Zhang, G. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3545905 · Cited by 6

Securing Federated Learning in IoT: A Survey of Attacks, Defenses, and Frameworks

Atia, O. B., Samara, M. A., Bennis, I., Gaber, J., Abouaïssa, A., & Lorenz, P. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3600617 · Cited by 6

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Likhitha, A., Mhatre, A., Nair, A. K., & Gupta, D. (2025) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri66576.2025.00043 · Cited by 6

Analysis on Security and Privacy-preserving in Federated Learning

Li, J., Li, X., & Zhang, C. (2022) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v4i.923 · Cited by 6

Attack-model-agnostic defense against model poisonings in distributed learning

Xu, H., & Shu, T. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103739 · Cited by 6

A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems

Ahmed, K. M., Das, B. C., Saadati, Y., & Amini, M. H. (2024) · Big and integrated artificial intelligence · doi:10.1007/978-3-031-57567-9_4 · Cited by 6

Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain

Du, B., Wang, H., Yuxuan, L., Zhao, J., Ma, Y., & Huang, R. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3673656 · Cited by 6

DM-FedMF: A Recommendation Model of Federated Matrix Factorization With Detection Mechanism

Zheng, X., Jia, X., Cheng, X., He, W., Sun, L., Guo, L., Yu, Q., & Luo, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3551923 · Cited by 6

Privacy-Enhanced Data Fusion for Federated Learning Empowered Internet of Things

Lin, Q., Xu, K., Huang, Y., Yu, F., & Wang, X. (2022) · Mobile Information Systems · doi:10.1155/2022/3850246 · Cited by 6

Enhancing Privacy in Federated Learning with Local Differential Privacy for Email Classification

Löbner, S., Gogov, B., & Tesfay, W. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25734-6_1 · Cited by 6

Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning

Aristodemou, M., Liu, X., Lambotharan, S., & AsSadhan, B. (2023) · IEEE Access · doi:10.1109/access.2023.3304541 · 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

EMDG-FL: Enhanced Malicious Model Detection based on Genetic Algorithm for Federated Learning

Atia, O. B., Samara, M. A., Bennis, I., Gaber, J., Abouaïssa, A., & Lorenz, P. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570752 · Cited by 7

TrustFed: A Reliable Federated Learning Framework With Malicious-Attack Resistance

Hang, S., Zhou, J., Feng, G., & Niu, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3510567 · Cited by 9

OCTOPUS: Overcoming Performance and Privatization Bottlenecks in Distributed Learning

Wang, S., Nepal‬, ‪., Moore, K., Grobler, M. M., Rudolph, C., & Abuadbba, A. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3157258 · Cited by 6

Fabricated Flips: Poisoning Federated Learning without Data

Huang, J., Zhao, Z., Chen, L. Y., & Roos, S. (2023) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58367.2023.00036 · Cited by 6

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

Ali, A. M., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2021) · doi:10.36227/techrxiv.14945433.v1 · Cited by 6

Secure Edge Intelligence in the 6G Era

Kumar, T., Partala, J., Nguyen, T., Agrawal, L., Mondal, A., Kumar, A., Ahmad, I., Peltonen, E., Pirttikangas, S., & Harjula, E. (2024) · Wiley eBooks · doi:10.1002/9781119988007.ch2 · Cited by 6

Oblivion: Poisoning Federated Learning by Inducing Catastrophic Forgetting

Zhang, C., Zhou, B., He, Z., Liu, Z., Chen, Y., Xu, W., & Li, B. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10228981 · Cited by 5

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 17

Attacking Byzantine Robust Aggregation in High Dimensions

Choudhary, S., Kolluri, A., & Saxena, P. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00217 · Cited by 9

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

FLARE: A Backdoor Attack to Federated Learning with Refined Evasion

Wang, Q., Wu, Y., Xuan, H., & Wu, H. (2024) · Mathematics · doi:10.3390/math12233751 · Cited by 5

Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things

Uprety, A., Rawat, D. B., & Sadler, B. M. (2025) · ACM Transactions on Internet of Things · doi:10.1145/3722562 · Cited by 5

Metaverse meets distributed machine learning: A contemporary review on the development with privacy-preserving concerns

Gardner, S. H., Hoang, T.-M., Na, W., Dao, N., & Cho, S. (2025) · ICT Express · doi:10.1016/j.icte.2025.04.008 · 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

Toward Malicious Clients Detection in Federated Learning

Dou, Z., Wang, J., Sun, W., Liu, Z., & Fang, M. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3736194 · Cited by 8

UDFed: A Universal Defense Scheme for Various Poisoning Attacks on Federated Learning

Deng, J., Li, C., Zhang, N., Yang, J., & Gao, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3611126 · Cited by 5

A lightweight secret-sharing-based defense against model poisoning attacks in privacy-preserving federated learning

Xiong, H., Lv, J., Man, D., Zhu, Y., Liu, T., Wang, H., Xu, C., & Yang, W. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108272 · Cited by 5

FedAssess: analysis for efficient communication and security algorithms over various federated learning frameworks and mitigation of label-flipping attack

Anusuya, R., & Renuka, D. K. (2023) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2024.148944 · Cited by 7

Investigating How Data Poising Attacks Can Impact An EEG-Based Federated Learning Model

Alshebli, S., Alshehhi, M., & Yeun, C. Y. (2024) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr61006.2024.10532875 · Cited by 5

FedDP-SA: Boosting Differentially Private Federated Learning via Local Data Set Splitting

Liu, X., Zhou, Y., Wu, D., Hu, M., Wang, J. H., & Guizani, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421991 · Cited by 5

Logits Poisoning Attack in Federated Distillation

Tang, Y., Wu, Z., Gao, B., Wen, T., Wang, Y., & Sun, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5498-4_22 · Cited by 5

Demo: Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning

Zhang, X., Zhang, J., Chow, K.-H., Chen, J., Mao, Y., Rahouti, M., Li, X., Liu, Y., & Wei, W. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00139 · Cited by 5

Distributed Recommendation Systems: Survey and Research Directions

Cai, Q., Cao, J., Xu, G., & Zhu, N. (2024) · ACM Transactions on Information Systems · doi:10.1145/3694783 · Cited by 6

Deep behavioral analysis of machine learning algorithms against data poisoning

Paracha, A., Arshad, J., Farah, M. A. B., & Ismail, K. N. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00940-x · Cited by 6

Sppdp-fl: adaptive client selection and dimensional projection for performance enhancement in personalized differential privacy federated learning

Xiao, J., Fan, J., & Lu, H. (2024) · Cluster Computing · doi:10.1007/s10586-024-04845-1 · Cited by 5

Robust and privacy-preserving federated learning scheme based on ciphertext-selected users

Wang, X., Liu, Z., & Huang, B. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111072 · Cited by 5

Adaptive Layered-Trust Robust Defense Mechanism for Personalized Federated Learning

Wang, H., Xu, Z., Zhang, Y., & Wang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887951 · 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

Linkage on security, privacy and fairness in federated learning: New balances and new perspectives

Wang, L., Zhu, T., Zhou, W., & Yu, P. S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107874 · Cited by 11

TIMPANY—deTectIon of Model Poisoning Attacks usiNg accuracY

Sameen, M., & Hwang, S. O. (2021) · IEEE Access · doi:10.1109/access.2021.3118926 · Cited by 5

AFCS: Aggregation-Free Spatial-Temporal Mobile Community Sensing

Bian, J., Xiong, H., Wang, Z., Zhou, J., Ji, S., Chen, H., Zhang, D., & Dou, D. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3178885 · Cited by 5

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

STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning

Wen, Y., Geiping, J., Goldblum, M., & Goldstein, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096606 · Cited by 5

Secure vertical federated learning based on feature disentanglement

Zhang, F., Li, G., Suo, Z., Wang, L., Cui, C., & Meng Qingshu, B. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109077 · Cited by 5

A Privacy Robust Aggregation Method Based on Federated Learning in the IoT

Li, Q., Wang, X., & Ren, S. (2023) · Electronics · doi:10.3390/electronics12132951 · Cited by 6

RAI4IoE: Responsible AI for Enabling the Internet of Energy

Xue, M., Nepal‬, ‪., Liu, L., Sethuvenkatraman, S., Yuan, X., Rudolph, C., Sun, R., & Eisenhauer, G. (2023) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa58951.2023.00012 · Cited by 6

Decentralized Defense: Leveraging Blockchain against Poisoning Attacks in Federated Learning Systems

Thennakoon, R., Wanigasundara, A., Weerasinghe, S., Seneviratne, C., Siriwardhana, Y., & Liyanage, M. (2024) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51664.2024.10454688 · Cited by 5

Distributed Attacks over Federated Reinforcement Learning-Enabled Cell Sleep Control

Zhang, H., Zhou, H., Elsayed, M., Bavand, M., Gaigalas, R., Ozcan, Y., & Erol‐Kantarci, M. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10464431 · Cited by 5

Data Poisoning Against Federated Learning: Comparative Analysis Under Label-Flipping Attacks and GAN-Generated EEG Data

Alsereidi, M., Awadallah, A., Alkaabi, A., Yoon, S.-Y., & Yeun, C. Y. (2024) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr61006.2024.10533012 · Cited by 5

Histopathological Image Classification and Vulnerability Analysis using Federated Learning

Vyas, S., Patra, A. N., & Shukla, R. M. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00334 · Cited by 5

Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning

Jiang, Y., Ma, B., Wang, X., Yu, G., Sun, C., Ni, W., & Liu, R. P. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128317 · Cited by 5

Large-Scale Mean-Field Federated Learning for Detection and Defense: A Byzantine Robustness Approach in IoT

Sun, K., Liu, L., Pan, Q., Li, J., & Wu, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3409610 · Cited by 6

Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework

Yang, Y., Li, Q., Nie, C., Hong, Y., & Wang, B. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679566 · Cited by 5

Biometrics and Artificial Intelligence: Attacks and Challenges

Hernández–Álvarez, L., González‐Manzano, L., Fuentes, J. M. D., & Encinas, L. H. (2022) · Springer eBooks · doi:10.1007/978-3-031-10706-1_10 · Cited by 5

Secure Federated Training: Detecting Compromised Nodes and Identifying the Type of Attacks

Ovi, P. R., Gangopadhyay, A., Erbacher, R. F., & Busart, C. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00183 · Cited by 6

Data Poisoning Attack Using Hybrid Particle Swarm Optimization in Connected and Autonomous Vehicles

Cui, C., Du, H., Jia, Z., He, Y., Yang, Y., & Jin, M. (2022) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde56538.2022.10089285 · Cited by 5

A Security-Oriented Overview of Federated Learning Utilizing Layered Reference Model

Lu, J., Fukumoto, N., & Nakao, A. (2024) · IEEE Access · doi:10.1109/access.2024.3404948 · Cited by 5

Is Shapley Value fair? Improving Client Selection for Mavericks in Federated LearningPreprint

Huang, J., Hong, C., Chen, L. Y., & Roos, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10734 · Cited by 12

Explainability-Aware Adversarial Threats and Mitigation in Federated Learning Based Anomaly Detection for Cooperative Smart Farming

Praharaj, L., Gupta, M., & Gupta, D. (2025) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec65595.2025.11119362 · Cited by 4

FedSECA: Sign Election and Coordinate-Wise Aggregation of Gradients for Byzantine Tolerant Federated Learning

Benjamin, J. G., Asokan, M., Yaqub, M. K., & Nandakumar, K. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00165 · Cited by 4

Targeted Adversarial Poisoning Attack Against Robust Aggregation in Federated Learning for Smart Grids

Zhang, G., & Sikdar, B. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3629243 · Cited by 4

When AI reviews science: Can we trust the referee?

Wang, J., Liu, Y., Xu, H., Hu, K., Di, S., Ni, W., Yue, L., Zhang, M.-L., Ren, K., & Chen, L. (2026) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2026.100030 · Cited by 7

Privacy-Preserving Detection of Poisoning Attacks in Federated Learning

Muhr, T., & Zhang, W. (2022) · Conference on Privacy, Security and Trust · doi:10.1109/pst55820.2022.9851993 · Cited by 4

Web 3.0 security: Backdoor attacks in federated learning-based automatic speaker verification systems in the 6G era

Wu, Y., Chen, J., Lei, T., Jia-hua, Y., & Hossain, M. S. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.022 · Cited by 4

Adaptive Selection of Loss Function for Federated Learning Clients Under Adversarial Attacks

Lee, S. (2024) · IEEE Access · doi:10.1109/access.2024.3426534 · Cited by 4

Defense-Guided Adaptive Attack on Byzantine-Robust Federated Learning

Hu, C., Liu, Y., Zhang, M., & Yang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0151-6_7 · Cited by 4

Trust Online Over-the-Air Computation for Wireless Federated Learning

Sun, M., Zheng, J., Du, H., Zhang, H., Niyato, D. T., Kang, J., Wang, J., Ren, J., Gao, L. C., & Wang, Z. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3547148 · Cited by 4

Robust Federated Learning Algorithm Based on Chaotic Encryption and Dual-Server Detection

Kang, S., Wu, Y., Wang, Y., Wang, Q., & Liang, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3565598 · Cited by 4

Mitigating malicious model fusion in federated learning via confidence-aware defense

Li, Q., Papageorgiou, P., Liu, G., Gao, M., You, L., Wang, C., & Abdelmoniem, A. M. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103529 · Cited by 9

zkFL: Verifiable Byzantine-Robust Federated Learning Against Malicious Servers

Tang, X., Li, M., Zhang, T., Lin, Y., Zhu, L., Zhou, C., & Liu, X. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3622060 · Cited by 4

Byzantine detection for federated learning under highly non-IID data and majority corruptions

Wang, Z., & Zhao, P. (2024) · Wireless Networks · doi:10.1007/s11276-024-03799-x · Cited by 5

Trust-Based Anomaly Detection in Federated Edge Learning

Zatsarenko, R., Chuprov, S., Korobeinikov, D., & Reznik, L. (2024) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot61789.2024.10578967 · Cited by 4

Federated Learning with Flexible Architectures

Park, J.-I., & Joe‐Wong, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70344-7_9 · Cited by 4

Joint Layer Selection and Differential Privacy Design for Federated Learning Over Wireless Networks

Ding, Y., Shang, W., Yang, Y., Ding, W., & Shikh‐Bahaei, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3452549 · Cited by 5

Analyzing Poisoning Attacks on Non-IID Federated Learning Systems for Credit Scoring

Patil, A., Choudhar, A., Shah, D., Abraham, J., & Bochare, A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724119 · Cited by 4

Poisoning Attacks on Federated-learning based NIDS

Mbow, M., Takahashi, T., & Sakurai, K. (2024) · International Symposium on Computing and Networking Workshops (CANDARW) · doi:10.1109/candarw64572.2024.00020 · Cited by 4

FLGT: label-flipping-robust federated learning via guiding trust

Li, H., Shi, Z., Jin, M., Yin, A., & Zhao, Z. W. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-024-02323-z · Cited by 4

Detecting Diverse Poisoning Attacks in Federated Learning Based on Joint Similarity

Deng, J., Liu, S., & Li, C. (2024) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp62071.2024.10827693 · Cited by 4

Cooperating or Kicking Out: Defending Against Poisoning Attacks in Federated Learning via the Evolution of Cooperation

Ye, D., Zhu, T., Gao, K., Zhu, C., & Zhou, W. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3532351 · Cited by 4

Mitigation of Sybil-based Poisoning Attacks in Permissionless Decentralized Learning

Mosqueda-González, B. A., Hasan, O., & Brunie, L. (2025) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc64466.2025.11114604 · Cited by 4

Robust Federated Learning Against Data Poisoning: A Split Learning-Based Approach Evaluated on Various Aggregation Techniques

Tounsi, A., Salem, O., & Mehaoua, A. (2024) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60970.2024.10880725 · Cited by 4

My Model is Malware to You: Transforming AI Models into Malware by Abusing TensorFlow APIs

Zhu, R., Chen, G., Shen, W., Xie, X., & Chang, R. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00012 · Cited by 4

Time-Distributed Backdoor Attacks on Federated Spiking Learning

Abad, G., Picek, S., & Urbieta, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07884-1_1 · Cited by 6

TEE-based Selective Testing of Local Workers in Federated Learning Systems

Zhang, W., & Muhr, T. (2021) · Conference on Privacy, Security and Trust · doi:10.1109/pst52912.2021.9647773 · Cited by 3

Privacy-Preserving Object Detection With Poisoning Recognition for Autonomous Vehicles

Li, J., Guo, W., Xie, L., Liu, X., & Cai, J. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3227119 · Cited by 4

An Adaptive Model Filtering Algorithm Based on Grubbs Test in Federated Learning

Yao, W., Pan, B., Hou, Y., Li, X., & Xia, Y. (2023) · Entropy · doi:10.3390/e25050715 · Cited by 4

SPoiL: Sybil-Based Untargeted Data Poisoning Attacks in Federated Learning

Lian, Z., Zhang, C., Nan, K., & Su, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39828-5_13 · Cited by 4

Nacc-Guard: a lightweight DNN accelerator architecture for secure deep learning

Li, P., Che, C., & Hou, R. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05671-9 · Cited by 4

Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things

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Test-Time Poisoning Attacks Against Test-Time Adaptation Models

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On the Efficiency of Privacy Attacks in Federated Learning

Tabassum, N., Chow, K.-H., Wang, X., Zhang, W., & Wu, Y. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00426 · Cited by 6

Data Poisoning Attack and Defenses in Connectome-Based Predictive Models

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(2023) · doi:10.1145/3591197 · Cited by 4

FedVPS: Federated Learning for Privacy and Security of Internet of Vehicles on Non-IID Data

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On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks Against LLMs

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FedCon: A Model Consistency-Based Mechanism to Safeguard Federated Learning in Vulnerable and Heterogeneous IoT Environments

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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

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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

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