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 · see citing works

PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Learning Multiple Layers of Features from Tiny Images

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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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Untitled work

(2017) · neural information processing systems · Cited by 1,413 · see citing works

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 · see citing works

Poisoning Attacks against Support Vector Machines

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

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

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works

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

Shafahi, A., Huang, W., Najibi, M., Suciu, O., Studer, C., Dumitraş, T., & Goldstein, T. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000460348 · Cited by 626 · see citing works

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549 · see citing works

SoK: Security and Privacy in Machine Learning

Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. P. (2018) · 2018 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2018.00035 · Cited by 502 · see citing works

Certified Defenses for Data Poisoning Attacks

Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1706.03691 · Cited by 479 · see citing works

Torchvision the machine-vision package of torch

Marcel, S., & Rodriguez, Y. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874254 · Cited by 430 · see citing works

SoK: Towards the Science of security and privacy in machine learning

Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 427 · see citing works

Demystifying Membership Inference Attacks in Machine Learning as a Service

Truex, S., Liu, L., Gürsoy, M. E., Yu, L., & Wei, W. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2897554 · Cited by 389 · see citing works

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare

Kermani, M. M., Sur‐Kolay, S., Raghunathan, A., & Jha, N. K. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2344095 · Cited by 383 · see citing works

ANTIDOTE: understanding and defending against poisoning of anomaly detectors

Rubinstein, B. I. P., Nelson, B., Huang, L., Joseph, A. D., Lau, S.-H., Rao, S., Taft, N., & Tygar, J. D. (2009) · Proceedings of the 9th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1644893.1644895 · Cited by 331 · see citing works

Exploiting machine learning to subvert your spam filter

Nelson, B. A., Barreno, M., Chi, F. J., Joseph, A. D., Rubinstein, B. I. P., Saini, U., Sutton, C. A., Tygar, J. D., & Xia, K. (2008) · Cited by 322 · see citing works

Support Vector Machines Under Adversarial Label Noise

Biggio, B., Nelson, B. A., & Laskov, P. (2011) · UNICA IRIS Institutional Research Information System (University of Cagliari) · Cited by 319 · see citing works

Is feature selection secure against training data poisoning?Preprint

Huang, X. X., Biggio, B., Gavin Brown, B., Fumera, G., Eckert, C. M., & Roli, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07933 · Cited by 314 · see citing works

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

Chen, S., Xue, M., Fan, L., Hao, S., Xu, L., Zhu, H., & Li, B. (2017) · Computers & Security · doi:10.1016/j.cose.2017.11.007 · Cited by 240 · see citing works

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

Shen, S., Tople, S., & Saxena, P. (2016) · Proceedings of the 32nd Annual Conference on Computer Security Applications · doi:10.1145/2991079.2991125 · Cited by 228 · see citing works

Support vector machines under adversarial label contamination

Huang, X. X., Biggio, B., Nelson, B. A., Xiao, H., Eckert, C. M., & Roli, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.081 · Cited by 220 · see citing works

Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning AttacksPreprint

Demontis, A., Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., & Roli, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02861 · Cited by 219 · see citing works

Poisoning Attacks to Graph-Based Recommender Systems

Fang, M., Yang, G., Gong, N. Z., & Liu, J. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274706 · Cited by 211 · see citing works

Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System

Nguyen, T. D., Rieger, P., Miettinen, M., & Sadeghi, A.-R. (2020) · Proceedings 2020 Workshop on Decentralized IoT Systems and Security · doi:10.14722/diss.2020.23003 · Cited by 200 · see citing works

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

Zhu, C., Huang, W., Shafahi, A., Li, H., Taylor, G., Studer, C., & Goldstein, T. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000459774 · Cited by 190 · see citing works

Analyzing Federated Learning through an Adversarial Lens

Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. B. (2019) · International Conference on Machine Learning · Cited by 188 · see citing works

Adversarial Label Flips Attack on Support Vector Machines

Xiao, H., Huang, X. X., & Claudia, E. (2012) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-098-7-870 · Cited by 180 · see citing works

Label Sanitization Against Label Flipping Poisoning Attacks

Paudice, A., Muñoz-González, L., & Lupu, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-13453-2_1 · Cited by 167 · see citing works

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

Suciu, O., Mărginean, R., Kaya, Y., Daumé, H., & Dumitraş, T. (2018) · arXiv (Cornell University) · Cited by 150 · see citing works

Let's Talk About Race

Schlesinger, A., O'Hara, K. P., & Taylor, A. S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173889 · Cited by 142 · see citing works

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionPreprint

Paudice, A., Muñoz-González, L., György, A., & Lupu, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03041 · Cited by 138 · see citing works

Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

Zhao, L., Hu, S., Wang, Q., Jiang, J., Chao, S., Luo, X., & Hu, P. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.2986205 · Cited by 127 · see citing works

Fake Co-visitation Injection Attacks to Recommender Systems

Yang, G., Gong, N. Z., & Cai, Y. (2017) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2017.23020 · Cited by 118 · see citing works

Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach

Baracaldo, N., Chen, B., Ludwig, H., & Safavi, J. A. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140450 · Cited by 108 · see citing works

Towards Adversarial Malware Detection

Maiorca, D., Biggio, B., & Giacinto, G. (2019) · ACM Computing Surveys · doi:10.1145/3332184 · Cited by 106 · see citing works

Is data clustering in adversarial settings secure?

Biggio, B., Pillai, I., Bulò, S. R., Ariu, D., Pelillo, M., & Roli, F. (2013) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2517312.2517321 · Cited by 105 · see citing works

Robust Linear Regression Against Training Data Poisoning

Liu, C., Li, B., Vorobeychik, Y., & Oprea, A. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140447 · Cited by 84 · see citing works

Efficient Label Contamination Attacks Against Black-Box Learning Models

Zhao, M., An, B., Gao, W., & Zhang, T. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/551 · Cited by 60 · see citing works

MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning

Khazbak, Y., Tan, T., & Cao, G. (2020) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn49398.2020.9209670 · Cited by 50 · see citing works

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Cao, X., Fang, M., Liu, J., & Gong, N. Z. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24434 · Cited by 999 · see citing works

Federated learning for smart healthcare: A survey

Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827 · see citing works

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 419 · see citing works

A robust privacy-preserving federated learning model against model poisoning attacks

Yazdinejad, A., Dehghantanha, A., Karimipour, H., Srivastava, G., & Parizi, R. M. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3420126 · Cited by 380 · see citing works

ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169918 · Cited by 371 · see citing works

From distributed machine learning to federated learning: a survey

Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01664-x · Cited by 374 · see citing works

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345 · see citing works

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

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works

Decentralized Federated Learning: A Survey and Perspective

Yuan, L., Wang, Z., Sun, L., Yu, P. S., & Brinton, C. G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3407584 · Cited by 324 · see citing works

Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

Miao, Y., Liu, Z., Li, H., Choo, K.-K. R., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3196274 · Cited by 303 · see citing works

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296 · see citing works

A survey of federated learning for edge computing: Research problems and solutions

Xia, Q., Ye, W., Tao, Z., Wu, J., & Li, Q. (2021) · High-Confidence Computing · doi:10.1016/j.hcc.2021.100008 · Cited by 290 · see citing works

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

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833647 · Cited by 292 · see citing works

Recent advances on federated learning: A systematic survey

Liu, B., Lv, N., Guo, Y., & Li, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128019 · Cited by 244 · 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

MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

Cao, X., & Gong, N. Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00383 · Cited by 205 · see citing works

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 209 · 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

Federated Learning: Opportunities and ChallengesPreprint

Mammen, P. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.05428 · Cited by 176 · see citing works

Privacy and Security in Federated Learning: A Survey

Gosselin, R., Vieu, L., Loukil, F., & Benoît, A. (2022) · Applied Sciences · doi:10.3390/app12199901 · Cited by 171 · see citing works

Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

Ferrag, M. A., Friha, O., Kantarcı, B., Tihanyi, N., Cordeiro, L. C., Debbah, M. A., Hamouda, D., Al-Hawawreh, M. S., & Choo, K. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3317242 · Cited by 170 · see citing works

LoMar: A Local Defense Against Poisoning Attack on Federated Learning

Li, X., Qu, Z., Zhao, S., Tang, B., Lu, Z., & Liu, Y. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135422 · Cited by 152 · see citing works

Emerging trends in federated learning: from model fusion to federated X learning

Ji, S., Tan, Y., Saravirta, T., Yang, Z. Q., Liu, Y., Vasankari, L., Pan, S., Long, G., & Walid, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02119-1 · Cited by 171 · 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 &amp; Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works

An overview of implementing security and privacy in federated learning

Hu, K., Gong, S., Zhang, Q., Seng, C., Xia, M., & Jiang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10846-8 · Cited by 162 · see citing works

Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

Bao, G., & Guo, P. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00377-4 · Cited by 131 · see citing works

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130 · see citing works

Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

Kalapaaking, A. P., Khalil, I., & Yi, X. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3268186 · Cited by 122 · see citing works

Detection and Mitigation of Label-Flipping Attacks in Federated Learning Systems with KPCA and K-Means

Li, D., Wong, W. E., Wang, W., Yao, Y., & Chau, M. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00081 · Cited by 115 · see citing works

Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Wang, W., Memon, F. H., Lian, Z., Yin, Z., Gadekallu, T. R., Pham, Q., Dev, K., & Su, C. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3116917 · Cited by 112 · see citing works

FL-Defender: Combating targeted attacks in federated learning

Jebreel, N. M., & Domingo-Ferrer, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110178 · Cited by 110 · see citing works

SecFedNIDS: Robust defense for poisoning attack against federated learning-based network intrusion detection system

Zhang, Z., Zhang, Y., Guo, D., Yao, L. Y., & Zhao, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.04.010 · Cited by 104 · see citing works

Threats to Training: A Survey of Poisoning Attacks and Defenses on Machine Learning Systems

Wang, Z., Ma, J., Wang, X., Hu, J., Qin, Z., & Ren, K. (2022) · ACM Computing Surveys · doi:10.1145/3538707 · Cited by 107 · see citing works

Clustered Vehicular Federated Learning: Process and Optimization

Taïk, A., Mlika, Z., & Cherkaoui, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3149860 · Cited by 104 · see citing works

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 108 · see citing works

RoFL: Robustness of Secure Federated Learning

Lycklama, H., Burkhalter, L., Viand, A., Küchler, N., & Hithnawi, A. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179400 · Cited by 114 · see citing works

SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning

Xiao, X., Tang, Z., Li, C., Xiao, B., & Li, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172310 · Cited by 99 · see citing works

Data Quality Detection Mechanism Against Label Flipping Attacks in Federated Learning

Jiang, Y., Zhang, W., & Chen, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3249568 · Cited by 98 · see citing works

A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges

Maniriho, P., Mahmood, A. N., & Chowdhury, M. J. M. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.030 · Cited by 97 · see citing works

Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems

Lu, R., Zhang, W., Wang, Y., Li, Q., Zhong, X., Yang, H., & Wang, D. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3240767 · Cited by 97 · 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

Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges

Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022) · Security and Communication Networks · doi:10.1155/2022/2886795 · Cited by 94 · see citing works

Efficient and Secure Federated Learning Against Backdoor Attacks

Miao, Y., Xie, R., Li, X., Liu, Z., Choo, K. R., & Deng, R. H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3354736 · Cited by 94 · see citing works

FLCert: Provably Secure Federated Learning Against Poisoning Attacks

Cao, X., Zhang, Z., Jia, J., & Gong, N. Z. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3212174 · Cited by 92 · 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

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

Ioannou, I. I., Nagaradjane, P., Angın, P., Balasubramanian, P., Kavitha, K. J., Murugan, P., & Vassiliou, V. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.02.023 · Cited by 89 · see citing works

Privacy-preserving edge federated learning for intelligent mobile-health systems

Aminifar, A., Shokri, M., & Aminifar, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.035 · Cited by 99 · see citing works

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

Uddin, M. P., Xiang, Y., Hasan, M., Bai, J., Zhao, Y., & Gao, L. (2025) · ACM Computing Surveys · doi:10.1145/3727643 · Cited by 94 · see citing works

A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

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CBRFL: A framework for Committee-based Byzantine-Resilient Federated Learning

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Using Highly Compressed Gradients in Federated Learning for Data Reconstruction Attacks

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A Detailed Survey on Federated Learning Attacks and Defenses

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Learn to Forget: Machine Unlearning via Neuron Masking

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Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

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Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority

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Advances in Robust Federated Learning: A Survey With Heterogeneity Considerations

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Serverless on Machine Learning: A Systematic Mapping Study

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Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges

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Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition

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Blockchain-Enabled: Multi-Layered Security Federated Learning Platform for Preserving Data Privacy

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Distributed and Secure Federated Learning for Wireless Computing Power Networks

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APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning

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Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures

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Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

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Detection and Mitigation of Targeted Data Poisoning Attacks in Federated Learning

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CONTRA: Defending Against Poisoning Attacks in Federated Learning

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