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20092022
most citedByzantine Multi-Agent Optimization: Part II

33 citations · 254 across the 68 of their papers we have counts for

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Showing 2019 · cs.DCShow all

6 papers · 2 filters

cs.DC2019★ 5 cited

Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning

Nirupam Gupta, Nitin H. Vaidya

This report considers the problem of Byzantine fault-tolerance in synchronous parallelized learning that is founded on the parallelized stochastic gradient descent (parallelized-SG…

cs.DC2019★ 1 cited

Exact Byzantine Consensus on Arbitrary Directed Graphs under Local Broadcast Model

Muhammad Samir Khan, Lewis Tseng, Nitin H. Vaidya

We consider Byzantine consensus in a synchronous system where nodes are connected by a network modeled as a directed graph, i.e., communication links between neighboring nodes are…

cs.DC2019★ 2 cited

Asynchronous Byzantine Consensus on Undirected Graphs under Local Broadcast Model

Muhammad Samir Khan, Nitin Vaidya

In this work we look at Byzantine consensus in asynchronous systems under the local broadcast model. In the local broadcast model, a message sent by any node is received identicall…

cs.DC2019

Exact Byzantine Consensus on Undirected Graphs under Local Broadcast Model

Muhammad Samir Khan, Syed Shalan Naqvi, Nitin H. Vaidya

This paper considers the Byzantine consensus problem for nodes with binary inputs. The nodes are interconnected by a network represented as an undirected graph, and the system is a…

cs.DC2019★ 2 cited

Byzantine Consensus under Local Broadcast Model: Tight Sufficient Condition

Muhammad Samir Khan, Nitin H. Vaidya

In this work we consider Byzantine Consensus on undirected communication graphs under the local broadcast model. In the classical point-to-point communication model the messages ex…

cs.DC2019★ 8 cited

Distributed Learning with Adversarial Agents Under Relaxed Network Condition

Pooja Vyavahare, Lili Su, Nitin H. Vaidya

This work studies the problem of non-Bayesian learning over multi-agent network when there are some adversarial (faulty) agents in the network. At each time step, each non-faulty a…