Fetching the paper…
Reading the bibliography…
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious.
M. Barborak, A. Dahbura, and M. Malek, “The consensus problem in fault-tolerant computing,” ACM Computing Surveys (CSur) , vol. 25, no. 2, pp. 171–220, 1993
1993
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database,” Available: http://yann. lecun. com/exdb/mnist , 1998
1998
Earlier work this paper cites.
B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, B. I. P. Rubinstein, U. Saini, C. Sutton, J. D. Tygar, and K. Xia., “Exploiting machine learning to subvert your spam filter,” in LEET , 2008
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
B. I. Rubinstein, B. Nelson, L. Huang, A. D. Joseph, S.-h. Lau, S. Rao, N. Taft, and J. Tygar, “Antidote: understanding and defending against poisoning of anomaly detectors,” in ACM IMC , 2009
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in ICML , 2012
2012
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A public domain dataset for human activity recognition using smartphones.” in ESANN , 2013
2013
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. N. Kather, C.-A. Weis, F. Bianconi, S. M. Melchers, L. R. Schad, T. Gaiser, A. Marx, and F. G. Zöllner, “Multi-class texture analysis in colorectal cancer histology,” Scientific reports , vol. 6, p. 27988, 2016
2016
Earlier work this paper cites.
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in NIPS Workshop on Private Multi-Party Machine Learning , 2016
2016
Earlier work this paper cites.
B. Li, Y. Wang, A. Singh, and Y. Vorobeychik, “Data poisoning attacks on factorization-based collaborative filtering,” in NIPS , 2016
2016
Earlier work this paper cites.
Federated Learning: Collaborative Machine Learning without Centralized Training Data . [Online]. Available: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
2017
Earlier work this paper cites.
P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in NIPS , 2017
2017
Earlier work this paper cites.
X. Chen, C. Liu, B. Li, K. Lu, and D. Song, “Targeted backdoor attacks on deep learning systems using data poisoning,” in arxiv , 2017
2017
Earlier work this paper cites.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” in POMACS , 2017
2017
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” in Machine Learning and Computer Security Workshop , 2017
2017
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AISTATS , 2017
2017
Cited alongside, same era.
L. Muñoz-González, B. Biggio, A. Demontis, A. Paudice, V. Wongrassamee, E. C. Lupu, and F. Roli, “Towards poisoning of deep learning algorithms with back-gradient optimization,” in AISec , 2017
2017
Cited alongside, same era.
Y. Nesterov and V. Spokoiny, “Random gradient-free minimization of convex functions,” vol. 17, no. 2. Springer, 2017, pp. 527–566
2017
Cited alongside, same era.
H. Xiao, K. Rasul, and R. Vollgraf. (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
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 AAAI , vol. 33, 2019, pp. 1544–1551
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Rajput, H. Wang, Z. Charles, and D. Papailiopoulos, “Detox: A redundancy-based framework for faster and more robust gradient aggregation,” in NIPS , 2019, pp. 10 320–10 330
2019
Later among the works it cites.
M. J. Wainwright, “High-dimensional statistics: A non-asymptotic viewpoint,” vol. 48. Cambridge University Press, 2019
2019
Later among the works it cites.
B. Wang and N. Z. Gong, “Attacking graph-based classification via manipulating the graph structure,” in CCS , 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
G. Yang, N. Z. Gong, and Y. Cai, “Fake co-visitation injection attacks to recommender systems,” in NDSS , 2017
2017
Cited alongside, same era.
M. Fang, G. Yang, N. Z. Gong, and J. Liu, “Poisoning attacks to graph-based recommender systems,” in Proceedings of the 34th Annual Computer Security Applications Conference , 2018, pp. 381–392
2018
Cited alongside, same era.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in NDSS , 2018
2018
Cited alongside, same era.
E. M. E. Mhamdi, R. Guerraoui, and S. Rouault, “The hidden vulnerability of distributed learning in byzantium,” in ICML , 2018
2018
Cited alongside, same era.
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein, “Poison frogs! targeted clean-label poisoning attacks on neural networks,” in NIPS , 2018
2018
Cited alongside, same era.
O. Suciu, R. Marginean, Y. Kaya, H. D. III, and T. Dumitras, “When does machine learning fail? generalized transferability for evasion and poisoning attacks,” in USENIX Security Symposium , 2018
2018
Cited alongside, same era.
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in ICML , 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
C. Xie, S. Koyejo, and I. Gupta, “Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,” in ICML , 2019, pp. 6893–6901
2019
Later among the works it cites.
H. Yang, X. Zhang, M. Fang, and J. Liu, “Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach,” in CDC . IEEE, 2019, pp. 5832–5837
2019
Later among the works it cites.
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
Later among the works it cites.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in AISTATS , 2020, pp. 2938–2948
2020
Closest in time.
M. Fang, X. Cao, J. Jia, and N. Z. Gong, “Local model poisoning attacks to byzantine-robust federated learning,” in USENIX Security Symposium , 2020
2020
Closest in time.
M. Fang, N. Z. Gong, and J. Liu, “Influence function based data poisoning attacks to top-n recommender systems,” in Proceedings of The Web Conference 2020 , 2020, pp. 3019–3025
2020
Closest in time.
J. Jia, B. Wang, X. Cao, and N. Z. Gong, “Certified robustness of community detection against adversarial structural perturbation via randomized smoothing,” in Proceedings of The Web Conference 2020 , 2020, pp. 2718–2724
2020
Closest in time.
2020
Closest in time.
C. Xie, K. Huang, P.-Y. Chen, and B. Li, “Dba: Distributed backdoor attacks against federated learning,” in ICLR , 2020
2020
Closest in time.