Defending local poisoning attacks in multi-party learning via immune system

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · 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

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 · see citing works

The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

Deng, L. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2211477 · Cited by 4,858 · see citing works

Federated Learning: Strategies for Improving Communication Efficiency

Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works

Federated Learning with Non-IID Data

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works

A survey on federated learning

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899 · see citing works

Untitled work

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

Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2983860 · Cited by 885 · 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

Characteristic analysis of Otsu threshold and its applications

Xu, X., Xu, S., Jin, L., & Song, E. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.01.021 · Cited by 638 · see citing works

Peptidoglycan recognition by the innate immune system

Wolf, A. J., & Underhill, D. M. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.136 · Cited by 469 · see citing works

A novel genetic algorithm based on immunity

Jiao, L., & Wang, L. (2000) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.867862 · Cited by 454 · see citing works

An Empirical Study of Example Forgetting during Deep Neural Network LearningPreprint

Toneva, M., Sordoni, A., Combes, R. T. D., Trischler, A., Bengio, Y., & Gordon, G. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05159 · Cited by 421 · see citing works

Mitigating Sybils in Federated Learning Poisoning

Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385 · see citing works

Approximated and User Steerable tSNE for Progressive Visual Analytics

Pezzotti, N., Lelieveldt, B. P. F., van der Maaten, L., Höllt, T., Eisemann, E., & Vilanova, A. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2570755 · Cited by 306 · 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

Threats to federated learning A survey

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

Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!

Yang, D., Fankhauser, B., Rosso, P., & Cudré-Mauroux, P. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/302 · Cited by 197 · see citing works

Variational Autoencoder for Semi-Supervised Text Classification

Xu, W., Sun, H., Deng, C., & Tan, Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10966 · Cited by 186 · see citing works

Deep Reinforcement Learning for Partially Observable Data Poisoning Attack in Crowdsensing Systems

Li, M., Sun, Y., Lu, H. D., Maharjan, S., & Tian, Z. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2962914 · Cited by 144 · see citing works

A clonal selection algorithm for optimization in electromagnetics

Campelo, F., Guimarães, F. G., Igarashi, H., & Ramírez, J. A. (2005) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2005.846043 · Cited by 129 · see citing works

An efficient proactive artificial immune system based anomaly detection and prevention system

Saurabh, P., & Verma, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.042 · Cited by 97 · see citing works

Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system

Fotohi, R. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106675 · Cited by 79 · see citing works

Attack-Resistant Federated Learning with Residual-based ReweightingPreprint

Fu, S., Xie, C., Li, B., & Chen, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11464 · Cited by 78 · see citing works

An efficient negative selection algorithm with further training for anomaly detection

Gong, M., Zhang, J., Ma, J., & Jiao, L. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.01.004 · Cited by 77 · see citing works

Incipient fault detection and estimation based on Jensen–Shannon divergence in a data-driven approach

Zhang, X., Delpha, C., & Diallo, D. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107410 · Cited by 52 · see citing works

A modified immune network algorithm for multimodal electromagnetic problems

Campelo, F., Guimarães, F. G., Igarashi, H., Ramírez, J. A., & Noguchi, S. (2006) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2006.871633 · Cited by 48 · see citing works

An artificial immune system for offline isolated handwritten arabic character recognition

Boufenar, C., Batouche, M., & Schoenauer, M. (2016) · Evolving Systems · doi:10.1007/s12530-016-9169-1 · Cited by 35 · see citing works

Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks

WAKUI, T., Hashiguchi, M., Sawada, K., & Yokoyama, R. (2018) · Energy · doi:10.1016/j.energy.2018.12.104 · Cited by 33 · see citing works

A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Farzadnia, E., Shirazi, H. M., & Nowroozi, A. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102721 · Cited by 28 · see citing works

Sentiment Lexicon Enhanced Attention-Based LSTM for Sentiment Classification

Lei, Z., Yang, Y., & Yang, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12142 · Cited by 26 · see citing works

Artificial immune system based approach for size and location optimization of distributed generation in distribution system

Bhadoria, V. S., Pal, N. S., & Shrivastava, V. (2019) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-019-00779-9 · Cited by 23 · see citing works

Transfer learning based countermeasure against label flipping poisoning attack

Chan, P. P. K., Luo, F., Chen, Z., Shu, Y., & Yeung, D. S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.10.016 · Cited by 17 · see citing works

Sequence based prediction of pattern recognition receptors by using feature selection technique

Feng, P., & Feng, L. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.06.234 · Cited by 10 · see citing works

An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response

Zhang, R., & Xiao, X. (2019) · Journal of Information Processing Systems · doi:10.3745/jips.03.0108 · Cited by 3 · 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

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 · see citing works

SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments

Cao, Y., Zhang, J., Zhao, Y., Su, P., & Huang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122410 · Cited by 43 · see citing works

MagicGAN: Multiagent Attacks Generate Interferential Category via GAN

Chen, Y. X., Wang, X., Hu, P., & Peng, D. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110023 · Cited by 7 · see citing works

Phase transition behavior of finite clusters under localized attack

Qing, T., Dong, G., Wang, F., Du, R., & Tian, L. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0079489 · Cited by 6 · see citing works

Accountable federated learning against local poisoning attacks

Wang, Y., Gao, Y., Zhang, Y., Geng, H., & Chi, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114828 · Cited by 4 · see citing works

HDP-FedCD: Data-quality-driven hierarchical federated learning for optimizing privacy protection in non-IID data

Yin, C., He, K., & Shi, J. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108140 · Cited by 2 · see citing works

SpyShield: a Spyfall inspired defense mechanism against poisoning attacks in federated learning

Elgharieb, Y., Alexan, W., El-Aasser, M., & Ghantous, M. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-16158-3 · Cited by 2 · see citing works

Construction of Safety-Management Platform for Chemical Enterprises Based on the Immune System Model

Yuan, X., Wang, B., Guan, X., Wang, Y., Othmane, C., Zhang, J., & Chen, X. (2022) · Applied Sciences · doi:10.3390/app12115522 · Cited by 1 · see citing works

Universal Multi-Party Poisoning AttacksPreprint

Mahloujifar, S., Mahmoody, M., & Mohammed, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03474 · Cited by 9 · see citing works

Contamination Attacks and Mitigation in Multi-Party Machine LearningPreprint

Hayes, J., & Ohrimenko, O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.02402 · Cited by 51 · see citing works

Towards multi-party targeted model poisoning attacks against federated learning systems

Chen, Z., Pu, T., Liao, W., & Yu, W. (2021) · High-Confidence Computing · doi:10.1016/j.hcc.2021.100002 · Cited by 30 · see citing works

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 · see citing works

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning AttacksPreprint

Paracha, A., Arshad, J., Farah, M. B., & Ismail, K. N. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.22274