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Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location.
W. Hoeffding, “Probability inequalities for sums of bounded random variables,” J. American stat. assoc. , vol. 58, no. 301, pp. 13–30, 1963
1963
Earlier work this paper cites.
L. Lamport, R. Shostak, and M. Pease, “The Byzantine generals problem,” ACM Trans. Programming Languages and Syst. , vol. 4, no. 3, pp. 382–401, 1982
1982
Earlier work this paper cites.
M. J. Fischer, N. A. Lynch, and M. S. Paterson, “Impossibility of distributed consensus with one faulty process,” J. ACM , vol. 32, no. 2, pp. 374–382, 1985
1985
Earlier work this paper cites.
V. Vapnik, “Principles of risk minimization for learning theory,” in Proc. Advances in Neural Information Processing Systems (NIPS’92) , 1992, pp. 831–838
1992
Earlier work this paper cites.
——, The Nature of Statistical Learning Theory , 2nd ed. New York, NY: Springer-Verlag, 1999
1999
Earlier work this paper cites.
K. Driscoll, B. Hall, H. Sivencrona, and P. Zumsteq, “Byzantine fault tolerance, from theory to reality,” in Proc. Int. Conf. Computer Safety, Reliability, and Security (SAFECOMP’03) , 2003, pp. 235–248
2003
Earlier work this paper cites.
Y. M. Minsky and F. B. Schneider, “Tolerating malicious gossip,” Distributed Computing , vol. 16, no. 1, pp. 49–68, 2003
2003
Earlier work this paper cites.
H. H. Sohrab, Basic Real Analysis , 2nd ed. New York, NY: Springer, 2003
2003
Earlier work this paper cites.
K. Driscoll, B. Hall, M. Paulitsch, P. Zumsteq, and H. Sivencrona, “The real Byzantine generals,” in Proc. Digital Avionics Syst. Conf. (DASC’04) , 2004, pp. 1–11
2004
Earlier work this paper cites.
P. Dutta, R. Guerraoui, and M. Vukolic, “Best-case complexity of asynchronous Byzantine consensus,” EPFL/IC/200499, Tech. Rep., 2005
2005
Earlier work this paper cites.
J. Verger-Gaugry, “Covering a ball with smaller equal balls in R n R^{n} ,” Discrete & Computational Geometry , vol. 33, no. 1, pp. 143–155, 2005
2005
Earlier work this paper cites.
J. B. Predd, S. B. Kulkarni, and H. V. Poor, “Distributed learning in wireless sensor networks,” IEEE Signal Process. Mag. , vol. 23, no. 4, pp. 56–69, 2006
2006
Earlier work this paper cites.
A. Nedić and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Trans. Autom. Control , vol. 54, no. 1, pp. 48–61, 2009
2009
Earlier work this paper cites.
S. Shalev-Shwartz, O. Shamir, N. Srebro, and K. Sridharan, “Stochastic convex optimization,” in Proc. Conf. Learning Theory (COLT’09) , Jun. 2009
2009
Earlier work this paper cites.
S. S. Ram, A. Nedić, and V. Veeravalli, “Distributed stochastic subgradient projection algorithms for convex optimization,” J. Optim. Theory and Appl. , vol. 147, no. 3, pp. 516–545, 2010
2010
Earlier work this paper cites.
P. A. Forero, A. Cano, and G. B. Giannakis, “Consensus-based distributed support vector machines,” J. Mach. Learning Research , vol. 11, pp. 1663–1707, 2010
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. and Trends Mach. Learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Earlier work this paper cites.
A. Rawat, P. Anand, H. Chen, and P. Varshney, “Collaborative spectrum sensing in the presence of Byzantine attacks in cognitive radio networks,” IEEE Trans. Signal Process. , vol. 59, no. 2, pp. 774–786, Feb. 2011
2011
Earlier work this paper cites.
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning . Cambridge, MA: MIT Press, 2012
2012
Cited alongside, same era.
J. Sousa and A. Bessani, “From Byzantine consensus to BFT state machine replication: A latency-optimal transformation,” in Proc. 9th Euro. Dependable Computing Conf. (EDCC’12) , 2012, pp. 37–48
2012
Cited alongside, same era.
N. H. Vaidya, L. Tseng, and G. Liang, “Iterative approximate Byzantine consensus in arbitrary directed graphs,” in Proc. ACM Symp. Principles of Distributed Computing , 2012, pp. 365–374
2012
Cited alongside, same era.
2012
Cited alongside, same era.
H. Raja and W. U. Bajwa, “Cloud K-SVD: A collaborative dictionary learing algorithm for big, distributed data,” IEEE Trans. Signal Process. , vol. 64, no. 1, pp. 173–188, Jan. 2016
2016
Later among the works it cites.
C. Planiden and X. Wang, “Strongly convex functions, Moreau envelopes, and the generic nature of convex functions with strong minimizers,” SIAM J. Optim. , vol. 26, no. 2, pp. 1341–1364, 2016
2016
Later among the works it cites.
A. Mokhtari, Q. Ling, and A. Ribeiro, “Network Newton distributed optimization methods,” IEEE Trans. Signal Process. , vol. 65, no. 1, pp. 146–161, 2017
2017
Closest in time.
2017
Closest in time.
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2012
Cited alongside, same era.
J. F. Mota, J. M. Xavier, P. M. Aquiar, and M. Puschel, “D-ADMM: A communication-efficient distributed algorithm for separable optimization,” IEEE Trans. Signal Process. , vol. 61, no. 10, pp. 2718–2723, 2013
2013
Cited alongside, same era.
A. Vempaty, L. Tong, and P. Varshney, “Distributed inference with Byzantine data: State-of-the-art review on data falsification attacks,” IEEE Signal Process. Mag. , vol. 30, no. 5, pp. 65–75, May 2013
2013
Cited alongside, same era.
H. J. LeBlanc, H. Zhang, X. Koutsoukos, and S. Sundaram, “Resilient asymptotic consensus in robust networks,” IEEE J. Sel. Areas in Commun. , vol. 31, no. 4, pp. 766–781, 2013
2013
Cited alongside, same era.
N. H. Vaidya and V. K. Garg, “Byzantine vector consensus in complete graphs,” in Proc. 2016 ACM Symp. Principles of Distributed Computing , 2013, pp. 65–73
2013
Cited alongside, same era.
W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, “On the linear convergence of the ADMM in decentralized consensus optimization,” IEEE Trans. Signal Process. , vol. 62, no. 7, pp. 1750–1761, 2014
2014
Cited alongside, same era.
N. H. Vaidya, L. Tseng, and G. Liang, “Iterative Byzantine vector consensus in incomplete graphs,” in Proc. 15th Int. Conf. Distributed Computing and Networking , 2014, pp. 14–28
2014
Cited alongside, same era.
2015
Cited alongside, same era.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” in Proc. ACM Measurement and Analysis of Computing Systems , vol. 1, no. 2, Dec. 2017, pp. 44:1–44:25
2017
Closest in time.
P. Blanchard, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Proc. Advances in Neural Inf. Process. Syst. , 2017, pp. 118–128
2017
Closest in time.
D. Dua and E. K. Taniskidou, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
2017
Closest in time.
Z. Yang and W. U. Bajwa, “ByRDiE: A Byzantine-resilient distributed learning algorithm,” in Proc. IEEE Data Science Workshop (DSW’18) , Lausanne, Switzerland, Jun. 2018, pp. 21–25
2018
Closest in time.
Y. Chen, S. Kar, and J. M. F. Moura, “Attack resilient distributed estimation: A consensus+innovations approach,” in Proc. Annu. American Control Conference (ACC’18) , Jun. 2018, pp. 1015–1020
2018
Closest in time.
G. Damaskinos, E. E. Mhamdi, R. Guerraoui, R. Patra, and M. Taziki, “Asynchronous Byzantine machine learning (the case of SGD),” in Proc. 35th Int. Conf. Machine Learning , vol. 80. PMLR, 2018, pp. 1145–1154
2018
Closest in time.
E. E. Mhamdi, R. Guerraoui, and S. Rouault, “The hidden vulnerability of distributed learning in Byzantium,” in Proc. 35th Int. Conf. Machine Learning , vol. 80. PMLR, 2018, pp. 3521–3530
2018
Closest in time.
2018
Closest in time.
L. Chen, H. Wang, Z. Charles, and D. Papailiopoulos, “DRACO: Byzantine-resilient distributed training via redundant gradients,” in Proc. 35th Intl. Conf. Machine Learning (ICML’18) , Jul. 2018, pp. 903–912
2018
Closest in time.
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in Proc. 35th Int. Conf. Machine Learning , vol. 80, 2018, pp. 5650–5659
2018
Closest in time.
D. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Proc. Advances in Neural Information Processing Systems , 2018, pp. 4618–4628
2018
Closest in time.
2018
Closest in time.