Moat: Model Agnostic Defense against Targeted Poisoning Attacks in Federated Learning

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

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 · see citing works

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 · see citing works

Untitled work

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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

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 of Deep Networks using Model AveragingPreprint

McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 923 · see citing works

DBA: Distributed Backdoor Attacks against Federated Learning

Xie, C., Huang, K., Chen, P., & Li, B. (2020) · International Conference on Learning Representations · Cited by 468 · see citing works

Understanding Distributed Poisoning Attack in Federated Learning

Cao, D., Chang, S., Lin, Z., Liu, G., & Sun, D. (2019) · 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads47876.2019.00042 · Cited by 285 · see citing works

Measure Contribution of Participants in Federated Learning

Wang, G., Dang, C. X., & Zhou, Z. Y. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006179 · Cited by 243 · see citing works

PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network

Zhao, Y., Chen, J., Zhang, J., Wu, D., Teng, J., & Yu, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38991-8_39 · Cited by 73 · see citing works

Privacy-preserving Decentralized Learning Framework for Healthcare System

Kasyap, H., & Tripathy, S. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3426474 · Cited by 56 · see citing works

Interpret Federated Learning with Shapley ValuesPreprint

Wang, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04519 · Cited by 51 · 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

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

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

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

Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.019 · Cited by 116 · see citing works

Beyond data poisoning in federated learning

Kasyap, H., & Tripathy, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121192 · Cited by 50 · see citing works

A systematic review of federated learning from clients’ perspective: challenges and solutions

Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 52 · see citing works

Privacy-preserving and Byzantine-robust Federated Learning Framework using Permissioned Blockchain

Kasyap, H., & Tripathy, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122210 · Cited by 41 · see citing works

Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

Nabi, S. A., Kalpana, P., Chandra, N. S., Smitha, L., Naresh, K., Ezugwu, A. E.-S., & Abualigah, L. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101547 · Cited by 27 · see citing works

Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack

Rezaie, H., Taheri, R., Jordanov, I., & Shojafar, M. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09963-8 · Cited by 25 · see citing works

An Efficient Blockchain Assisted Reputation Aware Decentralized Federated Learning Framework

Kasyap, H., Manna, A., & Tripathy, S. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3231283 · Cited by 25 · see citing works

SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks

Sandeepa, C., Siniarski, B., Wang, S., & Liyanage, M. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00271 · Cited by 23 · see citing works

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 27 · see citing works

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 13 · see citing works

Hidden Vulnerabilities in Cosine Similarity based Poisoning Defense

Kasyap, H., & Tripathy, S. (2022) · Conference on Information Sciences and Systems · doi:10.1109/ciss53076.2022.9751167 · Cited by 9 · see citing works

Exploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things

Li, K., Yuan, X., Zheng, J., Ni, W., & Guizani, M. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc58020.2023.10183217 · Cited by 8 · see citing works

Mitigation of a poisoning attack in federated learning by using historical distance detection

Shi, Z., Ding, X., Li, F., Chen, Y., & Li, C. (2022) · Annals of Telecommunications · doi:10.1007/s12243-022-00929-4 · Cited by 8 · see citing works

MILSA: Model Interpretation Based Label Sniffing Attack in Federated Learning

Manna, D., Kasyap, H., & Tripathy, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23690-7_8 · Cited by 7 · see citing works

A Secure and Lightweight Aggregation Method for Blockchain-based Distributed Federated Learning

Huang, H., Duan, L., Li, C., & Ni, W. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00066 · Cited by 5 · see citing works

HDFL: Private and Robust Federated Learning using Hyperdimensional Computing

Kasyap, H., Tripathy, S., & Conti, M. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00049 · Cited by 4 · see citing works

Preference Poisoning Attacks on Reward Model Learning

Wu, J., Wang, J., Xiao, C., Wang, C., Zhang, N., & Vorobeychik, Y. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00094 · Cited by 3 · see citing works

The Impact of Attacking Windows Using a Backdoor Trojan

Shen, K. J., & Selvarajah, V. (2023) · International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) · doi:10.1109/easct59475.2023.10393460 · Cited by 2 · see citing works

X-BDFL: Explainable Blockchain-Enabled Distributed Federated Learning Against Poisoning Attack in Healthcare

Roy, S., Singha, S. K., & Bera, D. (2025) · IEEE Access · doi:10.1109/access.2025.3648434

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 · see citing works

FLARE: Defending Federated Learning Against Model Poisoning Attacks via Latent Space Representations

Wang, N., Zhang, C., Xiao, Y., Chen, Y., Lou, W., & Hou, Y. T. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3521297 · Cited by 11 · see citing works

Evaluation of Various Defense Techniques Against Targeted Poisoning Attacks in Federated Learning

Richards, C., Khemani, S., & Li, F. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00102 · Cited by 5 · see citing works

FedDefender: Backdoor Attack Defense in Federated Learning

Gill, W., Anwar, A., & Gulzar, M. A. (2023) · doi:10.1145/3617574.3617858 · Cited by 12 · see citing works

Towards Mitigating Poisoning Attacks in Federated Learning

Elhattab, F., Talbi, R., Bouchenak, S., & Nitu, V. (2021) · HAL · Cited by 1 · see citing works