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Gradient-based training in federated learning is known to be vulnerable to faulty/malicious clients, which are often modeled as Byzantine clients.
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V. Shejwalkar and A. Houmansadr, “Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning,” ISOC Network and Distributed Systems Security (NDSS) Symposium , 2021
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X. Cao, M. Fang, J. Liu, and N. Z. Gong, “FLTrust: Byzantine-robust federated learning via trust bootstrapping,” ISOC Network and Distributed Systems Security (NDSS) Symposium , 2021
2021
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Z. Allen-Zhu, F. Ebrahimianghazani, J. Li, and D. Alistarh, “Byzantine-resilient non-convex stochastic gradient descent,” in International Conference on Learning Representations, ICLR , 2021
2021
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S. P. Karimireddy, L. He, and M. Jaggi, “Learning from history for byzantine robust optimization,” in Proceedings of International Conference on Machine Learning, ICML , 2021
2021
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E. El-Mhamdi, R. Guerraoui, and S. Rouault, “Distributed momentum for byzantine-resilient stochastic gradient descent,” in International Conference on Learning Representations (ICLR) , 2021
2021
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X. Cao, J. Jia, and N. Z. Gong, “Provably secure federated learning against malicious clients,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI , 2021
2021
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