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While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework.
“The Byzantine generals problem,”
L. Lamport, R. Shostak, and M. Pease, · 1982
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“Compressed sensing for networked data,”
J. Haupt, W. U. Bajwa, M. Rabbat, and R. Nowak, · 2008
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“Distributed subgradient methods for multi-agent optimization,”
A. Nedić and A. Ozdaglar, · 2009
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“Matrix representation of iterative approximate Byzantine consensus in directed graphs,”
N. Vaidya, · 2012
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“An adaptive deviation-tolerant secure scheme for distributed cooperative spectrum sensing,”
S. Liu, H. Zhu, S. Li, C. Chen, and X. Guan, · 2012
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“Distributed inference with Byzantine data: State-of-the-art review on data falsification attacks,”
A. Vempaty, L. Tong, and P. Varshney, · 2013
Earlier work this paper cites.
“Localization in wireless sensor networks: Byzantines and mitigation techniques,”
A. Vempaty, O. Ozdemir, K. Agrawal, H. Chen, and P. K. Varshney, · 2013
Earlier work this paper cites.
“Resilient asymptotic consensus in robust networks,”
H. J. LeBlanc, H. Zhang, X. Koutsoukos, and S. Sundaram, · 2013
Earlier work this paper cites.
“Distributed inference with M-ary quantized data in the presence of Byzantine attacks,”
V. S. S. Nadendla, Y. S. Han, and P. K. Varshney, · 2014
Earlier work this paper cites.
“Resilient distributed parameter estimation in heterogeneous time-varying networks,”
H. J. LeBlanc and F. Hassan, · 2014
Earlier work this paper cites.
L. Su and N. Vaidya, · 2015
Earlier work this paper cites.
“Distributed Bayesian detection in the presence of Byzantine data,”
B. Kailkhura, Y. S. Han, S. Brahma, and P. K. Varshney, · 2015
Earlier work this paper cites.
“Distributed detection in tree networks: Byzantines and mitigation techniques,”
B. Kailkhura, S. Brahma, B. Dulek, Y. S. Han, and P. K. Varshney, · 2015
Earlier work this paper cites.
“Approximate Byzantine consensus in faulty asynchronous networks,”
L. Haseltalab and M. Akar, · 2015
Earlier work this paper cites.
“Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,”
Y. Chen, L. Su, and J. Xu, · 2017
Cited alongside, same era.
“Machine learning with adversaries: Byzantine tolerant gradient descent,”
P. Blanchard, R. Guerraoui, and J. Stainer, · 2017
Cited alongside, same era.
“Stochastic and deterministic fault detection for randomized gossip algorithms,”
D. Silvestre, P. Rosa, J. P. Hespanha, and C. Silvestre, · 2017
Cited alongside, same era.
“Byzantine-resilient locally optimum detection using collaborative autonomous networks,”
B. Kailkhura, P. Ray, D. Rajan, A. Yen, P. Barnes, and R. Goldhahn, · 2017
Cited alongside, same era.
“Data falsification attacks on consensus-based detection systems,”
B. Kailkhura, S. Brahma, and P. K. Varshney, · 2017
Cited alongside, same era.
“The Internet of Things: Secure distributed inference,”
“Byzantine stochastic gradient descent,”
D. Alistarh, Z. Allen-Zhu, and J. Li, · 2018
Later among the works it cites.
“Resilient distributed estimation: Sensor attacks,”
Y. Chen, S. Kar, and J. M. F. Moura, · 2018
Later among the works it cites.
“Resilient distributed estimation: Exponential convergence under sensor attacks,”
Y. Chen, S. Kar, and J. M. F. Moura, · 2018
Later among the works it cites.
“Resilient distributed estimation through adversary detection,”
Y. Chen, S. Kar, and J. M. F. Moura, · 2018
Later among the works it cites.
“Fall of empires: Breaking Byzantine-tolerant SGD by inner product manipulation,”
C. Xie, S. Koyejo, and I. Gupta, · 2019
Closest in time.
“Local model poisoning attacks to Byzantine-robust federated learning,”
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Y. Chen, S. Kar, and J. M. F. Moura, · 2018
Cited alongside, same era.
“Approaches to secure inference in the Internet of Things: Performance bounds, algorithms, and effective attacks on IoT sensor networks,”
J. Zhang, R. S. Blum, and H. V. Poor, · 2018
Cited alongside, same era.
“Audit bit based distributed Bayesian detection in the presence of Byzantines,”
W. Hashlamoun, S. Brahma, and P. K. Varshney, · 2018
Cited alongside, same era.
“Byzantine-robust distributed learning: Towards optimal statistical rates,”
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, · 2018
Cited alongside, same era.
“The hidden vulnerability of distributed learning in Byzantium,”
E. El-Mhamdi, R. Guerraoui, and S. Rouault, · 2018
Cited alongside, same era.
“Zeno: Byzantine-suspicious stochastic gradient descent,”
C. Xie, O. Koyejo, and I. Gupta, · 2018
Cited alongside, same era.
“Generalized Byzantine-tolerant SGD,”
C. Xie, O. Koyejo, and I. Gupta, · 2018
Cited alongside, same era.
M. Fang, X. Cao, J. Jia, and N. Z. Gong, · 2019
Closest in time.
“A little is enough: Circumventing defenses for distributed learning,”
G. Baruch, M. Baruch, and Y. Goldberg, · 2019
Closest in time.
“Fast and secure distributed learning in high dimension,”
E. El-Mhamdi and R. Guerraoui, · 2019
Closest in time.
“Zeno++: Robust asynchronous SGD with arbitrary number of Byzantine workers,”
C. Xie, O. Koyejo, and I. Gupta, · 2019
Closest in time.
“RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,”
L. Li, W. Xu, T. Chen, G. Giannakis, and Q. Ling, · 2019
Closest in time.
“Distributed optimization under adversarial nodes,”
S. Sundaram and B. Gharesifard, · 2019
Closest in time.
“ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning,”
Z. Yang and W. U. Bajwa, · 2019
Closest in time.
“BRIDGE: Byzantine-resilient decentralized gradient descent,”
Z. Yang and W. U. Bajwa, · 2019
Closest in time.