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We consider the problem of Byzantine fault-tolerance in federated machine learning.
L. Lamport, R. Shostak, and M. Pease, “The Byzantine generals problem,” ACM Transactions on Programming Languages and Systems (TOPLAS) , vol. 4, no. 3, pp. 382–401, 1982
1982
Earlier work this paper cites.
C. Bajaj, “The algebraic degree of geometric optimization problems,” Discrete & Computational Geometry , vol. 3, no. 2, pp. 177–191, 1988
1988
Earlier work this paper cites.
2012
Earlier work this paper cites.
M. S. Chong, M. Wakaiki, and J. P. Hespanha, “Observability of linear systems under adversarial attacks,” in American Control Conference . IEEE, 2015, pp. 2439–2444
2015
Earlier work this paper cites.
L. Su and N. H. Vaidya, “Fault-tolerant multi-agent optimization: optimal iterative distributed algorithms,” in Proceedings of the 2016 ACM symposium on principles of distributed computing . ACM, 2016, pp. 425–434
2016
Earlier work this paper cites.
S. Mishra, Y. Shoukry, N. Karamchandani, S. N. Diggavi, and P. Tabuada, “Secure state estimation against sensor attacks in the presence of noise,” IEEE Transactions on Control of Network Systems , vol. 4, no. 1, pp. 49–59, 2016
2016
Earlier work this paper cites.
M. B. Cohen, Y. T. Lee, G. Miller, J. Pachocki, and A. Sidford, “Geometric median in nearly linear time,” in Proceedings of the forty-eighth annual ACM symposium on Theory of Computing , 2016, pp. 9–21
2016
Earlier work this paper cites.
P. Blanchard, R. Guerraoui, et al. , “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Advances in Neural Information Processing Systems , 2017, pp. 119–129
2017
Earlier work this paper cites.
M. Charikar, J. Steinhardt, and G. Valiant, “Learning from untrusted data,” in Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing , 2017, pp. 47–60
2017
Earlier work this paper cites.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” Proceedings of the ACM on Measurement and Analysis of Computing Systems , vol. 1, no. 2, pp. 1–25, 2017
2017
Earlier work this paper cites.
Z. Yang and W. U. Bajwa, “Byrdie: Byzantine-resilient distributed coordinate descent for decentralized learning,” 2017
2017
Earlier work this paper cites.
D. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 4618–4628
2018
Earlier work this paper cites.
R. Guerraoui, S. Rouault, et al. , “The hidden vulnerability of distributed learning in byzantium,” in International Conference on Machine Learning . PMLR, 2018, pp. 3521–3530
2018
Earlier work this paper cites.
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in International Conference on Machine Learning , 2018, pp. 5636–5645
2018
Earlier work this paper cites.
S. Sundaram and B. Gharesifard, “Distributed optimization under adversarial nodes,” IEEE Transactions on Automatic Control , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
K. Kuwaranancharoen, L. Xin, and S. Sundaram, “Byzantine-resilient distributed optimization of multi-dimensional functions,” in 2020 American Control Conference (ACC) . IEEE, 2020, pp. 4399–4404
2020
Later among the works it cites.
C. Xie, O. Koyejo, and I. Gupta, “Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation,” in Proceedings of The 35th Uncertainty in Artificial Intelligence Conference , ser. Proceedings of Machine Learning Research, R. P. Adams and V. Gogate, Eds., vol. 115. PMLR, 22–25 Jul 2020, pp. 261–270. [Online]. Available: https://proceedings.mlr.press/v115/xie20a.html
2020
Later among the works it cites.
J.-y. Sohn, D.-J. Han, B. Choi, and J. Moon, “Election coding for distributed learning: Protecting signsgd against byzantine attacks,” Advances in Neural Information Processing Systems , vol. 33, 2020
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
L. Su and S. Shahrampour, “Finite-time guarantees for byzantine-resilient distributed state estimation with noisy measurements,” IEEE Transactions on Automatic Control , vol. 65, no. 9, pp. 3758–3771, 2019
2019
Cited alongside, same era.
Z. Yang and W. U. Bajwa, “Byrdie: Byzantine-resilient distributed coordinate descent for decentralized learning,” IEEE Transactions on Signal and Information Processing over Networks , vol. 5, no. 4, pp. 611–627, 2019
2019
Cited alongside, same era.
L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling, “Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 1544–1551
2019
Cited alongside, same era.
I. Diakonikolas, G. Kamath, D. Kane, J. Li, J. Steinhardt, and A. Stewart, “Sever: A robust meta-algorithm for stochastic optimization,” in Proceedings of the 36th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 1596–1606. [Online]. Available: http://proceedings.mlr.press/v97/diakonikolas19a.html
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
A. Prasad, A. S. Suggala, S. Balakrishnan, and P. Ravikumar, “Robust estimation via robust gradient estimation,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 82, no. 3, pp. 601–627, 2020
2020
Later among the works it cites.
L. Su and N. H. Vaidya, “Byzantine-resilient multi-agent optimization,” IEEE Transactions on Automatic Control , 2020
2020
Later among the works it cites.
M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to byzantine-robust federated learning,” in 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) , 2020, pp. 1605–1622
2020
Later among the works it cites.
Z. Wu, Q. Ling, T. Chen, and G. B. Giannakis, “Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks,” IEEE Transactions on Signal Processing , vol. 68, pp. 4583–4596, 2020
2020
Later among the works it cites.
J. So, B. Güler, and A. S. Avestimehr, “Byzantine-resilient secure federated learning,” IEEE Journal on Selected Areas in Communications , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Kairouz and H. B. McMahan, “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1, 2021
2021
Closest in time.
2021
Closest in time.
E. M. E. Mhamdi, R. Guerraoui, and S. Rouault, “Distributed momentum for byzantine-resilient stochastic gradient descent,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=H8UHdhWG6A3
2021
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