Non-Bayesian Learning in the Presence of Byzantine Agents

By Lili Su, Nitin H. Vaidya. 2016

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Byzantine-Tolerant Machine Learning (2017, methodology): “In [21] the authors tackle a multi-dimensional situation, using an iterated approximate Byzantine agreement that reaches consensus asymptotically.”
Defending non-Bayesian learning against adversarial attacks (2016, neutral): “A short version of this manuscript [1] has been accepted to appear in the Proceedings of the International Symposium on Distributed Computing (DISC), Sep, 2016.”
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning (2018, background): “This Byzantine-robust distributed learning problem has attracted attention in a recent line of work [3, 10, 17, 26, 61, 62, 69].”
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent (2017, background): “In [29], the authors tackle a multi-dimensional situation, using an iterated approximate Byzantine agreement that reaches consensus asymptotically.”
On the Robustness of a Neural Network (2017, methodology): “This coarse-grained approach is the one taken by recent works on the robustness of machine learning [20], [21], [22].”
Defending Distributed Systems Against Adversarial Attacks: Consensus, Consensusbased Learning, and Statistical Learning (2020, methodology): “We proposed and analyzed a learning rule [18] wherein each agent updates its local pseudo beliefs as (up to normalization) the product of (A) the likelihood of the cumulative private signals and (B) the weighted geometric average of the beliefs of its incoming neighbors and itself (using Byzantine c”