A Taxonomy of Attacks on Federated Learning

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961

MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint

Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02249 · Cited by 2,523

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

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

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

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001

The security of machine learning

Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839

Membership Inference Attacks on Machine Learning: A Survey

Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 662

The Roadmap to 6G Security and Privacy

Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3078081 · Cited by 440

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

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

Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey

Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2022) · ACM Computing Surveys · doi:10.1145/3560816 · Cited by 347

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

A Survey of Privacy Attacks in Machine Learning

Rigaki, M., & Garcia, S. (2024) · ACM Computing Surveys · doi:10.1145/3624010 · Cited by 285

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

Machine Learning for Microcontroller-Class Hardware: A Review

Saha, S. S., Sandha, S. S., & Srivastava, M. B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210773 · Cited by 300

Vulnerabilities in federated learning

Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248

PPFL

Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., & Kourtellis, N. (2021) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3458864.3466628 · Cited by 322

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

Decentralized Federated Learning: A Survey on Security and Privacy

Hallaji, E., Razavi‐Far, R., Saif, M., Wang, B., & Yang, Q. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3362191 · Cited by 204

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 185

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

Membership Inference Attacks and Defenses in Federated Learning: A Survey

Bai, L., Hu, H., Ye, Q., Li, H., Wang, L., & Xu, J. (2024) · ACM Computing Surveys · doi:10.1145/3704633 · Cited by 179

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

Macas, M., Wu, C., & Fuertes, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122223 · Cited by 175

A privacy preserving framework for federated learning in smart healthcare systems

Wang, W., Li, X., Qiu, X., Zhang, X., Brusić, V., & Zhao, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103167 · Cited by 145

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Javeed, D., Saeed, M. S., Ahmad, I., Adil, M., Kumar, P., & Islam, A. K. M. N. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.023 · Cited by 143

An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning

Li, S., Ngai, E. C. ‐., & Voigt, T. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3237397 · Cited by 126

Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech

Dash, B., Sharma, P., & Ali, A. (2022) · International Journal of Software Engineering & Applications · doi:10.5121/ijsea.2022.13401 · Cited by 118

Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Khraisat, A., Alazab, A., Singh, S., Jan, T., & Gomez, A. J. (2024) · ACM Computing Surveys · doi:10.1145/3687124 · Cited by 152

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 114

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

Ding, Y., Yang, Z., Pham, Q., Hu, Y., Zhang, Z., & Shikh‐Bahaei, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3341307 · Cited by 123

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt, L., Heyer, M., Toyoda, K., Samek, W., & Li, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3231363 · Cited by 114

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijji, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10417-3 · Cited by 100

3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Li, H., Ye, Q., Hu, H., Li, J., Wang, L., Fang, C., & Shi, J. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179401 · Cited by 84

A robust analysis of adversarial attacks on federated learning environments

Nair, A. K., Raj, E. D., & Sahoo, J. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103723 · Cited by 70

Federated Learning as a Privacy Solution - An Overview

Khan, M., Glavin, F. G., & Nickles, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.227 · Cited by 75

FederatedTrust: A solution for trustworthy federated learning

Sánchez, P. M. S., Celdrán, A. H., Xie, N., Bovet, G., Pérez, G. M., & Stiller, B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.013 · Cited by 82

RanSAP: An open dataset of ransomware storage access patterns for training machine learning models

Hirano, M., Hodota, R., & Kobayashi, R. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301314 · Cited by 81

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

Xu, R., Li, B. H., Li, C., Joshi, J. B. D., Ma, S., & Li, J. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3350206 · Cited by 75

A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption

Arazzi, M., Nicolazzo, S., & Nocera, A. (2023) · Information Systems Frontiers · doi:10.1007/s10796-023-10443-0 · Cited by 75

Anomaly detection and defense techniques in federated learning: a comprehensive review

Zhang, C., Yang, S., Mao, L., & Ning, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10796-1 · Cited by 73

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning

Usynin, D., Ziller, A., Makowski, M. R., Braren, R., Rueckert, D., Glocker, B., Kaissis, G. A., & Passerat‐Palmbach, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00390-3 · Cited by 68

A Detailed Survey on Federated Learning Attacks and Defenses

Sikandar, H. S., Waheed, H., Tahir, S., Malik, S. U. R., & Rafique, W. (2023) · Electronics · doi:10.3390/electronics12020260 · Cited by 64

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

Li, K., Li, C., Yuan, X., Li, S., Zou, S., Ahmed, S. S., Ni, W., Niyato, D. T., Jamalipour, A., Dressler, F., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3603957 · Cited by 61

Federated Learning Attacks and Defenses: A Survey

Chen, Y., Gui, Y., Lin, H., Gan, W., & Wu, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020431 · Cited by 69

A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends

Luzón, M. V., Rodríguez-Barroso, N., Argente-Garrido, A., Jiménez-López, D., Moyano, J. M., Ser, J. D., Ding, W., & Herrera, F. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124215 · Cited by 64

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

Casaletto, J. A., Bernier, A., McDougall, R., & Cline, M. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-110122-084756 · Cited by 71

PPFL: Privacy-preserving Federated Learning with Trusted Execution EnvironmentsPreprint

Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., & Kourtellis, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14380 · Cited by 52

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51

Visual privacy attacks and defenses in deep learning: a survey

Zhang, G., Liu, B., Zhu, T., Zhou, A., & Zhou, W. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10123-y · Cited by 56

Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging

Koutsoubis, N., Waqas, A., Yılmaz, Y., Ramachandran, R. P., Schabath, M. B., & Rasool, G. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240637 · Cited by 70

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Majeed, A., Khan, S., & Hwang, S. O. (2022) · IEEE Access · doi:10.1109/access.2022.3175219 · Cited by 51

Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach

Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41

Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey

Wang, Z., Kang, Q., Zhang, X., & Hu, Q. (2022) · 2022 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc51071.2022.9771619 · Cited by 41

A blockchain-empowered federated learning-based framework for data privacy in lung disease detection system

Gupta, M., Kumar, M., & Gupta, Y. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108302 · Cited by 41

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

Valdez, W., Parra-Ullauri, J. M., & Kertész, A. (2024) · IEEE Access · doi:10.1109/access.2024.3398034 · Cited by 41

A survey on membership inference attacks and defenses in machine learning

Niu, J., Liu, P., Zhu, X., Shen, K., Wang, Y., Chi, H., Shen, Y., Jiang, X., Ma, J., & Zhang, Y. (2024) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2024.02.001 · Cited by 51

Investigating on the robustness of flow-based intrusion detection system against adversarial samples using Generative Adversarial Networks

Duy, P. T., Khoa, N. H., Hien, D. T. T., Hoang, H. D., & Pham, V.-H. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103472 · Cited by 37

RVE-PFL: Robust Variational Encoder-Based Personalized Federated Learning Against Model Inversion Attacks

Issa, W., Moustafa, N., Turnbull, B., & Choo, K. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3368879 · Cited by 30

A Novel Hybrid, BERT and Deep Learning Model Network Intrusion Detection System for Healthcare Electronics

Alferaidi, A., Yadav, K., Alharbi, Y. O., Alreshidi, E. J., Alreshidi, A., Aboshosha, B. W., Sharma, R., Alkhayyat, A. H., & Aray, D. G. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3412199 · Cited by 28

Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review

Gupta, M., Kumar, M., & Dhir, R. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100685 · Cited by 27

Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications

Alrubayyi, H., Alshareef, M. S., Nadeem, Z., Abdelmoniem, A. M., & Jaber, M. (2024) · Future Internet · doi:10.3390/fi16030085 · Cited by 25

Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges

Sandeepa, C., Zeydan, E., Samarasinghe, T., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3513832 · Cited by 33

Cybersecurity Issues in AI

Puthal, D., & Mohanty, S. P. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3066828 · Cited by 23

A Hybrid Recommendation System: A Review

Chaudhari, A. N., Seddig, A. A. H., Sarlan, A. B., & Raut, R. (2024) · IEEE Access · doi:10.1109/access.2024.3480693 · Cited by 26

Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups

Wainakh, A., Zimmer, E., Subedi, S., Keim, J., Grube, T., Karuppayah, S., Guinea, A. S., & Mühlhäuser, M. (2022) · Sensors · doi:10.3390/s23010031 · Cited by 26

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

Hallaji, E., Razavi‐Far, R., & Saif, M. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_3 · Cited by 24

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Vu, T. T., Ngo, H. Q., Dao, M. N., Ngo, D. T., Larsson, E. G., & Le‐Ngoc, T. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3222749 · Cited by 23

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Manzoor, S. I., Jain, S., Singh, Y. P., & Singh, H. (2023) · IEEE Access · doi:10.1109/access.2023.3269880 · Cited by 22

Federated learning for rare disease detection: a survey

Wang, J., & Ma, F. (2023) · Rare Disease and Orphan Drugs Journal · doi:10.20517/rdodj.2023.16 · Cited by 21

Blockchain-Based Federated Learning: A Systematic Survey

Huang, J., Kong, L., Chen, G., Xiang, Q., Chen, X., & Liu, X. (2022) · IEEE Network · doi:10.1109/mnet.129.2200346 · Cited by 21

GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems

Psychogyios, K., Velivassaki, T.-H., Bourou, S., Voulkidis, A. C., Skias, D., & Zahariadis, T. B. (2023) · Electronics · doi:10.3390/electronics12081805 · Cited by 24

Vulnerabilities of Data Protection in Vertical Federated Learning Training and Countermeasures

Zhu, D., Chen, J., Zhou, X., Shang, W., Hassan, A. E., & Großklags, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3361813 · Cited by 23

DART: A Solution for decentralized federated learning model robustness analysis

Feng, C., Celdrán, A. H., von der Assen, J., Beltrán, E. T. M., Bovet, G., & Stiller, B. (2024) · Array · doi:10.1016/j.array.2024.100360 · Cited by 23

Federation Opacity and the Promise of Federated Learning in Healthcare

Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 27

Building Trusted Federated Learning: Key Technologies and Challenges

Chen, D., Jiang, X., Zhong, H., & Cui, J. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12010013 · Cited by 21

Privacy-Preserving Federated Learning Approach for Distributed Malware Attacks With Intermittent Clients and Image Representation

Ullah, F., Srivastava, G., Ullah, S., & Mostarda, L. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3342644 · Cited by 20

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach

Agarwal, V., Mishra, S., & Pal, S. (2024) · ACM Transactions on Internet Technology · doi:10.1145/3680544 · Cited by 19

Turning Federated Learning Systems Into Covert Channels

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2022) · IEEE Access · doi:10.1109/access.2022.3229124 · Cited by 35

Resilience of Federated Learning Against False Data Injection Attacks in Energy Forecasting

Shabbir, A., Manzoor, H. U., Hablulmateen, R. A. A., & Halim, Z. (2024) · International Conference on Green Energy, Computing and Sustainable Technology (GECOST) · doi:10.1109/gecost60902.2024.10475064 · Cited by 17

CLSM-FL: Clustering-Based Semantic Federated Learning in Non-IID IoT Environment

Lee, H., & Seo, D. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3408149 · Cited by 18

Federated Learning in IoT Environments: Examining the Three-Way See-Saw for Privacy, Model-Performance, and Network-Efficiency

Laidi, R., Merabtine, N., Djenouri, D., Latif, S., Qadir, H. A., Djenouri, Y., & Balasingham, I. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3557475 · Cited by 16

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Wu, J., Jin, J., & Wu, C. (2024) · Mathematics · doi:10.3390/math12060901 · Cited by 15

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

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

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Shiri, I., Salimi, Y., Sirjani, N., Razeghi, B., Bagherieh, S., Pakbin, M., Mansouri, Z., Hajianfar, G., Avval, A. H., Askari, D., et al. (2024) · Medical Physics · doi:10.1002/mp.16964 · Cited by 17

Data Poisoning Detection in Federated Learning

Khuu, D.-P., Sober, M., Kaaser, D., Fischer, M., & Schulte, S. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3635896 · Cited by 15

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

Wang, S., Sahay, R., & Brinton, C. G. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279348 · Cited by 15

Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning

Li, S., Ngai, E. C. ‐., Ye, F., Ju, L., Zhang, T., & Voigt, T. (2024) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi61053.2024.00018 · Cited by 16

Federated Learning: Attacks, Defenses, Opportunities and Challenges

Shirvani, G., Ghasemshirazi, S., & Beigzadeh, B. (2024) · International Symposium on Telecommunications · doi:10.1109/ist64061.2024.10843494 · Cited by 20

VagueGAN: A GAN-Based Data Poisoning Attack Against Federated Learning Systems

Sun, W. J., Gao, B., Xiong, K., Lu, Y., & Wang, Y. (2023) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon58729.2023.10287523 · Cited by 13

Robust and privacy-preserving federated learning with distributed additive encryption against poisoning attacks

Zhang, F., Huang, H., Chen, Z., & Huang, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110383 · Cited by 15

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

Hwang, S. O., & Majeed, A. (2024) · Applied Sciences · doi:10.3390/app14104100 · Cited by 15

Gradient Obfuscation Gives a False Sense of Security in Federated Learning

Yue, K., Jin, R., Wong, C.-W., Baron, D., & Dai, H. (2022) · arXiv · doi:10.48550/arxiv.2206.04055 · Cited by 26

Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition

Mohammadi, S., Mohammadi, M., Sinaei, S., Balador, A., Nowroozi, E., Flammini, F., & Conti, M. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f444 · Cited by 12

Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City Applications

Utomo, S., Rouniyar, A., Hsu, H.-C., & Hsiung, P. (2023) · Future Internet · doi:10.3390/fi15110371 · Cited by 12

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Sánchez, P. M. S., Celdrán, A. H., Pérez, E. T. M., Demeter, D., Bovet, G., Pérez, G. M., & Stiller, B. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05510-1 · Cited by 13

MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing

Wang, R., Xu, C., Zhang, S., Ye, F., Tang, Y., Tang, S. H., Zhang, H., Du, W., & Zhang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-53431-x · Cited by 12

The Power of Bias: Optimizing Client Selection in Federated Learning With Heterogeneous Differential Privacy

Ma, J., Zhou, Y., Li, Q. L., Sheng, Q. Z., Cui, L., & Liu, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3572527 · Cited by 12

Covert Channel Attack to Federated Learning Systems.Preprint

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2021) · arXiv (Cornell University) · Cited by 12

A Secure Object Detection Technique for Intelligent Transportation Systems

Mia, M. J., & Amini, M. H. (2024) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2024.3440876 · Cited by 11

A more secure framework for open government data sharing based on federated learning

Zhang, X. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101981 · Cited by 13

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Altoub, M., Alqurashi, F. A., Yiğitcanlar, T., Corchado, J. M., & Mehmood, R. (2022) · Applied Sciences · doi:10.3390/app122111053 · Cited by 13

Federated Learning-Based Trust Evaluation With Fuzzy Logic for Privacy and Robustness in Fog Computing

Vinh, T. L., Huan, T. T., Phan, H. T., & Bouzefrane, S. (2025) · IEEE Access · doi:10.1109/access.2025.3596093 · Cited by 11

Accessibility of covariance information creates vulnerability in Federated Learning frameworks

Huth, M., Arruda, J., Gusinow, R., Contento, L., Tacconelli, E., & Hasenauer, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad531 · Cited by 12

An Investigation of Model Poisoning Dynamics in Federated Learning Scenarios

Megha, R., Thampi, A. A., Varma, V. B. C., & Sarath, G. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724613 · Cited by 9

Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition

Mohammadi, S., Sinaei, S., Balador, A., & Flammini, F. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00156 · Cited by 9

Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition

Mohammadi, S., Sinaei, S., Balador, A., & Flammini, F. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7032-2_1 · Cited by 9

Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions

Qammar, A., Ding, J., & Ning, H. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10098-w · Cited by 58

Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

Liu, P., Xu, X., & Wang, W. (2022) · Cybersecurity · doi:10.1186/s42400-021-00105-6 · Cited by 261

Security and Privacy in Federated Learning: A Survey and Unified Taxonomy of Threat Models, Attacks, and DefensesPreprint

Fenoglio, D., Gjoreski, M., Gabrielli, E., & Langheinrich, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6771005

Poisoning Attacks and Defenses in Federated Learning: A SurveyPreprint

Sagar, S., Li, C.-S., Loke, S. W., & Choi, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.05795 · Cited by 8

Security and privacy concerns in Federated Learning systems: a systematic reviewPreprint

Salvo, R. D., & Galletta, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7148932/v1 · Cited by 1