Threats to Federated Learning

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

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

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

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210

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

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956

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

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

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

A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

Li, W., Hacid, H., Almazrouei, E., & Debbah, M. A. (2023) · AI · doi:10.3390/ai4030039 · Cited by 37

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara, O., Avila, A. R., & Falk, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3355816 · Cited by 35

CoLL-IoT: A Collaborative Intruder Detection System for Internet of Things Devices

Alshahrani, H. M. (2021) · Electronics · doi:10.3390/electronics10070848 · Cited by 32

Learning Universal Adversarial Perturbation by Adversarial Example

Li, M., Yang, Y., Wei, K., Yang, X., & Huang, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20023 · Cited by 32

PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack

Wang, J., Chang, X., Mišić, J., Mišić, V. B., & Wang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288936 · Cited by 34

How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

Lyu, L., Li, Y., Nandakumar, K., Yu, J., & Ma, X. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3006287 · Cited by 31

ELXGB: An Efficient and Privacy-Preserving XGBoost for Vertical Federated Learning

Xu, W., Zhu, H., Zheng, Y., Wang, F., Zhao, J., Liu, Z., & Li, H. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3394706 · Cited by 31

Federated Learning for Urban Sensing Systems: A Comprehensive Survey on Attacks, Defences, Incentive Mechanisms, and Applications

Kapoor, A., & Kumar, D. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3434510 · Cited by 32

Practical Attribute Reconstruction Attack Against Federated Learning

Chen, C., Lyu, L., Yu, H., & Chen, G. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3159236 · Cited by 30

Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning

Mohammadabadi, S. M. S., Yang, L., Yan, F., & Zhang, J. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00069 · Cited by 30

Privacy preserving and secure robust federated learning: A survey

Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.8084 · Cited by 29

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

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

Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning

Enthoven, D., & Al-Ars, Z. (2022) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms58070.2022.10062088 · Cited by 28

FedComm: Federated Learning as a Medium for Covert Communication

Hitaj, D., Pagnotta, G., Hitaj, B., Pérez‐Cruz, F., & Mancini, L. V. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3288215 · Cited by 29

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Ziegler, J., Pfitzner, B., Schulz, H., Saalbach, A., & Bert Arnrich, A. (2022) · Sensors · doi:10.3390/s22145195 · Cited by 30

CFSL: A Credible Federated Self-Learning Framework

Zhang, W., Bao, Z., Liu, Y., Xu, L. J., Lu, Q., Ning, H., Wang, X., Yang, S., Wang, F., & Li, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3286398 · Cited by 27

Robust Federated Learning: Maximum Correntropy Aggregation Against Byzantine Attacks

Luan, Z., Li, W., Liu, M., & Chen, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3383294 · Cited by 27

Secure Federated Evolutionary Optimization—A Survey

Liu, Q., Yan, Y., Jin, Y., Wang, X., Ligeti, P., Yu, G., & Yan, X. (2023) · Engineering · doi:10.1016/j.eng.2023.10.006 · Cited by 28

A Review of Research on Secure Aggregation for Federated Learning

Zhang, X., Luo, Y., & Li, T. (2025) · Future Internet · doi:10.3390/fi17070308 · Cited by 30

A survey on group fairness in federated learning: challenges, taxonomy of solutions and directions for future research

Salazar, T., Araújo, H. J., Cano, A., & Abreu, P. H. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11475-5 · Cited by 33

Electronic Health Records as Source of Research Data

Wang, W., Ferrari, D., Haddon-Hill, G. W., & Ćurčin, V. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_11 · Cited by 26

Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

Han, S., Park, S., Wu, F., Kim, S., Zhu, B., Xie, X., & Cha, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00461 · Cited by 28

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

Novikova, E., Fomichov, D., Kholod, I., & Filippov, E. (2022) · Sensors · doi:10.3390/s22082983 · Cited by 24

A systematic review on federated learning system: a new paradigm to machine learning

Chaudhary, R. K., Kumar, R., & Saxena, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02257-6 · Cited by 24

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

TORR: A Lightweight Blockchain for Decentralized Federated Learning

Ma, X., & Xu, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288078 · Cited by 24

Leveraging small-scale datasets for additive manufacturing process modeling and part certification: Current practice and remaining gaps

Fullington, D., Yangue, E., Bappy, M. M., Liu, C., & Tian, W. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.04.021 · Cited by 23

SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework

Zhao, J., Zhu, H., Xu, W., Wang, F., Lu, R., & Li, H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3232955 · Cited by 23

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

Salama, A., Stergioulis, A., Hayajneh, A. M., Zaidi, S. A. R., McLernon, D., & Robertson, I. D. (2023) · IEEE Access · doi:10.1109/access.2023.3246924 · Cited by 24

Bilateral Privacy Protection Scheme Based on Adaptive Location Generalization and Grouping Aggregation in Mobile Crowdsourcing

Sun, X., Wang, Y., Duan, P., Zia, Q., Wang, W., & Cai, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358799 · Cited by 22

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

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

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

FLRAM: Robust Aggregation Technique for Defense against Byzantine Poisoning Attacks in Federated Learning

Chen, H., Chen, X., Peng, L., & Ma, R. (2023) · Electronics · doi:10.3390/electronics12214463 · Cited by 21

ROBY: A Byzantine-Robust and Privacy-Preserving Serverless Federated Learning Framework

Tang, X., Li, M., Shen, M., Kang, J., Zhu, L., Liu, Z., Yang, G., Niyato, D. T., & Deng, R. H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3589066 · Cited by 21

Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs

Soret, B., Nguyen, L. D., Seeger, J., Bröring, A., Issaid, C. B., Samarakoon, S., Elgabli, A., Kulkarni, V., Bennis, M., & Popovski, P. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3138792 · Cited by 21

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

Afzal, M. U., Abdellatif, A. A., Zubair, M. F., Mehmood, M. Q., & Massoud, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3323932 · Cited by 23

A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Tang, Y., Zhang, Y., Niu, T., Li, Z., Zhang, Z., Chen, H., & Zhang, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.030084 · Cited by 21

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

FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

Gao, X., Yang, X., Yu, H., Yan, K., & Li, T. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00424 · Cited by 20

A Review and Experimental Evaluation on Split Learning

Hu, Z., Zhou, T., Wu, B., Chen, C., & Wang, Y. (2025) · Future Internet · doi:10.3390/fi17020087 · Cited by 19

Unified Framework for Enhancing Federated Learning Security and Robustness using GANs, Blockchain, and Differential Privacy

Swapna, N. S., Muralidhar, A., Latha, K. M., Archana, M., Thirupathi, V., Bhavsingh, M., D, V. S. S., Addepalli, L., & Lloret, J. (2025) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece65353.2025.11035670 · Cited by 19

Visual Object Detection for Privacy-Preserving Federated Learning

Zhang, J., Zhou, J., Guo, J., & Sun, X. (2023) · IEEE Access · doi:10.1109/access.2023.3263533 · Cited by 19

Blockchain-based optimized edge node selection and privacy preserved framework for federated learning

Qammar, A., Naouri, A., Ding, J., & Ning, H. (2023) · Cluster Computing · doi:10.1007/s10586-023-04145-0 · Cited by 21

FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

Duy, P. T., Quyen, N. H., Khoa, N. H., Tran, T.-D., & Pham, V.-H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100966 · Cited by 19

Federated learning for misbehaviour detection with variational autoencoders and Gaussian mixture models

Campos, E. M., González-Vidal, A., Hernández-Ramos, J. L., & Skármeta, A. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01000-8 · Cited by 18

TBCIM: Two-Level Blockchain-Aided Edge Resource Allocation Mechanism for Federated Learning Service Market

Ma, L., Qian, Y., Yu, G., Li, Z., Wang, L., Li, Q., Wang, X., & Han, G. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3589017 · Cited by 20

FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models

Chen, H., Li, H., Zhang, Y., Bi, J., Zhang, G., Zhang, Y., Torr, P. H. S., Gu, J., Krompaß, D., & Volker Tresp, C. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02834 · Cited by 22

Privacy protection in cross-platform recommender systems: techniques and challenges

Sun, Z., Wang, Z., & Xu, Y. (2023) · Wireless Networks · doi:10.1007/s11276-023-03509-z · Cited by 18

FedPD: Federated Open Set Recognition with Parameter Disentanglement

Yang, C., Zhu, M., Liu, Y., & Yuan, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00450 · Cited by 19

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-Based Intrusion Detection in the Context of IIoT Networks: Poisoning Attack and Defense

Vy, N. C., Quyen, N. H., Duy, P. T., & Pham, V.-H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92708-0_8 · Cited by 18

A security-friendly privacy-preserving solution for federated learning

Karakoç, F., Karaçay, L., Çomak, P., Gülen, U., Fuladi, R., & Soykan, E. Ü. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.004 · Cited by 17

Siren+: Robust Federated Learning With Proactive Alarming and Differential Privacy

Guo, H., Wang, H., Song, T., Yang, H., Ma, R., Jin, X., Xue, Z., & Guan, H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3362534 · Cited by 17

A Comprehensive Survey on Energy Efficiency in Federated Learning: Strategies and Challenges

Gouissem, A., Chkirbene, Z., & Hamila, R. (2024) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon58629.2024.10488805 · Cited by 17

Infrastructure Assisted Autonomous Driving: Research, Challenges, and Opportunities

George, R., Clancy, J., Brophy, T., Sistu, G., O'Grady, W., Chandra, S., Collins, F., Mullins, D., Jones, E., Deegan, B. M., et al. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3542213 · Cited by 19

When Two-Layer Federated Learning and Mean-Field Game Meet 5G and Beyond Security: Cooperative Defense Systems for 5G and Beyond Network Slicing

Sedjelmaci, H., & Boualouache, A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3294568 · Cited by 16

A Game-theoretic Framework for Privacy-preserving Federated Learning

Zhang, X., Fan, L., Wang, S., Li, W., Chen, K., & Yang, Q. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3656049 · Cited by 21

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 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

Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks

Zahir-Ismail, A.-T., & Shukla, R. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-15993-8 · Cited by 16

A survey of privacy-preserving federated learning for intrusion detection systems

Bunko, T., Johnstone, M. N., Yang, W., & Scott, B. A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11519-4 · Cited by 17

Data Poisoning Attacks With Hybrid Particle Swarm Optimization Algorithms Against Federated Learning in Connected and Autonomous Vehicles

Cui, C., Du, H., Jia, Z., Zhang, X., He, Y., & Yang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3337638 · Cited by 16

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser, M., & Zinnikus, I. (2024) · Energies · doi:10.3390/en17091992 · Cited by 16

Asset-Centric Threat Modeling for AI-Based Systems

von der Assen, J., Sharif, J., Feng, C., Killer, C., Bovet, G., & Stiller, B. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679445 · Cited by 15

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data

Lee, J., & Kim, W. (2022) · Sensors · doi:10.3390/s22218263 · Cited by 15

Concepts, Key Challenges and Open Problems of Federated Learning

Iqbal, Z., & Chan, H. Y. (2021) · International Journal of Engineering · doi:10.5829/ije.2021.34.07a.11 · Cited by 14

Federated Learning for Tabular Data: Exploring Potential Risk to Privacy

Wu, H., Zhao, Z., Chen, L. Y., & van Moorsel, A. (2022) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre55969.2022.00028 · Cited by 16

AspectFL: Aspect-Oriented Programming for Trustworthy and Compliant Federated Learning Systems

Alsobeh, A., Shatnawi, A., & Magableh, A. A. A. R. (2025) · Information · doi:10.3390/info16121048 · Cited by 16

Federated Learning for 6G Security: A Survey on Threats, Solutions, and Research Directions

de Alwis, C., Aouedi, O., Xu, J., Wang, S., Siriwardhana, Y., Hewa, T. M., Zeydan, E., Sandeepa, C., & Liyanage, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3663434 · Cited by 16

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

FedDAA: a robust federated learning framework to protect privacy and defend against adversarial attack

Lu, S., Li, R., & Liu, W. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-023-2283-x · Cited by 13

PriRoAgg: Achieving Robust Model Aggregation With Minimum Privacy Leakage for Federated Learning

Hou, S., Li, S., Jahani-Nezhad, T., & Caire, G. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3577498 · Cited by 14

Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network

Akallouch, M., Akallouch, O., Fardousse, K., Bouhoute, A., & Berrada, I. (2022) · Information · doi:10.3390/info13080381 · Cited by 13

Personnel status detection model suitable for vertical federated learning structure

Ji, J., Yan, D., & Mu, Z. (2022) · 2022 The 6th International Conference on Machine Learning and Soft Computing · doi:10.1145/3523150.3523166 · Cited by 12

Energy-Efficient Wireless Federated Learning: A Secrecy Oriented Design via Sequential Artificial Jamming

Wang, T., Huang, N., Wu, Y., & Quek, T. Q. S. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3229277 · Cited by 12

AutoFL: Towards AutoML in a Federated Learning Context

Preuveneers, D. (2023) · Applied Sciences · doi:10.3390/app13148019 · Cited by 12

PASTEL

Elhattab, F., Bouchenak, S., & Boscher, C. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3633808 · Cited by 12

Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning

Wang, Y., Li, K., Luo, Y., Li, G., Guo, Y., & Wang, Z. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00182 · Cited by 13

Strategic Data Revocation in Federated Unlearning

Ding, N., Wei, E., & Berry, R. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621201 · Cited by 12

Securing Federated Learning: Approaches, Mechanisms and Opportunities

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2024) · Electronics · doi:10.3390/electronics13183675 · Cited by 12

Federated Learning for Advanced Manufacturing Based on Industrial IoT Data Analytics

Kanagavelu, R., Li, Z., Samsudin, J., Hussain, S., Yang, F., Yang, Y., Goh, R. S. M., & Cheah, M. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-67270-6_6 · Cited by 11

Distributed Learning in Healthcare

Tuladhar, A., Rajashekar, D., & Forkert, N. D. (2022) · Integrated science · doi:10.1007/978-3-031-11199-0_10 · Cited by 11

Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework

Mohamed, H., Koroniotis, N., Moustafa, N., Schiliro, F., & Zomaya, A. Y. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3492919 · Cited by 12

Pack: Towards Communication-Efficient Homomorphic Encryption in Federated Learning

Zuo, Z., Su, N., Li, B., & Zhang, T. (2024) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3698038.3698557 · Cited by 11

Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review

Ieropoulos, V., Anthi, E., Spyridopoulos, T., Burnap, P., Mavromatis, I., Khan, A., & Carnelli, P. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104127 · Cited by 11

VerifBFL: Leveraging zk-SNARKs for a Verifiable Blockchained Federated Learning

Bellachia, A. A., Bouchiha, M. A., Ghamri-Doudane, Y. M., & Rabah, M. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073628 · Cited by 10

Evil vs evil: using adversarial examples to against backdoor attack in federated learning

Liu, T., Li, M., Zheng, H., Ming, Z., & Chen, J. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00965-z · Cited by 10

Decentralized federated meta‐learning framework for few‐shot multitask learning

Li, X., Li, Y., Wang, J., Chen, C., Yang, L., & Zheng, Z. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22951 · Cited by 10

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

FedICU: a federated learning model for reducing the medication prescription errors in intensive care units

Pais, V., Rao, S., Muniyal, B., & Yun, S. (2024) · Cogent Engineering · doi:10.1080/23311916.2023.2301150 · Cited by 11

Iterative and mixed-spaces image gradient inversion attack in federated learning

Fang, L., Wang, L., & Li, H. (2024) · Cybersecurity · doi:10.1186/s42400-024-00227-7 · Cited by 11

An Robust Secure Blockchain-Based Hierarchical Asynchronous Federated Learning Scheme for Internet of Things

Chen, Y., Linglongyan, & Ai, D. (2024) · IEEE Access · doi:10.1109/access.2024.3493112 · Cited by 10

Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture

Fu, D. S., Huang, J., Hazra, D., Dwivedi, A. K., Gupta, S. K., Shivahare, B. D., & Garg, D. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303462 · Cited by 9

VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification

Cho, Y., Han, W., Yu, M., Lee, Y., Bae, H., & Paek, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70903-6_15 · Cited by 9

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

Usynin, D., Rueckert, D., Passerat‐Palmbach, J., & Kaissis, G. A. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2022-0014 · Cited by 10

Time-Efficient Blockchain-Based Federated Learning

Lin, R., Wang, F., Luo, S., Wang, X., & Moshe Zukerman, D. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3436862 · Cited by 8

Exploring Communication-Efficient Federated Learning via Stateless in-Network Aggregation

Xia, J., Cheng, G., Wu, W., Luo, L., & Guo, D. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3551368 · Cited by 8

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548

A survey on security and privacy threats to federated learning

Zhang, J., Li, M., Zeng, S., Xie, B., & Zhao, D. (2021) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana53684.2021.00062 · Cited by 34

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480

Data poisoning and adversarial attacks in federated learning

Jeelan, P. M., & Khan, K. N. (2026) · CRC Press eBooks · doi:10.1201/9781003660330-6