Broadcast Gossip Algorithms for Consensus

By Tuncer Can Aysal, Muhamet Yildiz, Anand D. Sarwate, Anna Scaglione. 2009

505 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Public Health, Environmental and Occupational Health

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Citations in the corpus

Asynchronous Decentralized Parallel Stochastic Gradient Descent (2017, methodology): “…studied by the control community for solving the consensus problem where the goal is to compute the mean of all the data distributed on multiple nodes [Aysal et al., 2009, Boyd et al., 2005, Carli et al., 2010, Fagnani and Zampieri, 2008, Olfati-Saber et al., 2007, Schenato and Gamba, 2007].”
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (2017, background): “…there are work on the consensus problem for which decentralized algorithms are studied to compute the mean of all the data distributed on multiple nodes [Aysal et al., 2009, Boyd et al., 2005, Carli et al., 2010, Fagnani and Zampieri, 2008, Olfati-Saber et al., 2007, Schenato and Gamba, 2007].”
Personalized and Private Peer-to-Peer Machine Learning (2017, methodology): “For communication, we rely on a broadcast-based model (Aysal et al., 2009; Nedic, 2011) where agents send messages to all”
On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers (2013, background): “ design local deterministic or random update rules to achieve global coordination (for deterministic update rules, see [4], [7], [15], [23], [27], [38], [39], [40], [44]; for random update rules, see [1], [5], [9], [14]). August 1, 2013 DRAFT 5 methods typically have a convergence rate of O(1/ √ k).”
Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization (2011, methodology): “• Our assumptions encompass the case of non-doubly stochastic matrices and, as a particular case, the natural broadcast scheme of [3].”
2016 Ieee International Conference on Big Data (big Data) Consensus Optimization with Delayed and Stochastic Gradients on Decentralized Networks (unknown): “A number of algorithms for broadcast consensus are developed, for instance, in [17].”
A new perspective on randomized gossip algorithms (2016, unknown): “See also [9, 10, 11].”
Hierarchical Control for Multiple DC-Microgrids Clusters (2014, background): “introduced in the literature from which consensus-based [15] and gossip [16] algorithms have recently received significant attention mostly because of their simplicity and robustness for”
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018, background): “One particular choice of the weights in C t that relies on only local information of the agents is known as Metropolis weights (Xiao et al., 2005; Cattivelli et al., 2008) (Boyd et al., 2006), broadcast gossip (Aysal et al., 2009), and network dropouts (Bianchi et al., 2013).”
Adaptive Networks (2014, background): “One simple strategy is to pick one neighbor at a time randomly, which is the case with useful gossip implementations for distributed processing [88], [90], [148]–[152].”
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip (2019, methodology): “Future work inludes the design of privacy preserving variants of several popular and fast gossip protocols [7, 127, 82, 19, 106].”
Asynchronous Broadcast-Based Convex Optimization Over a Network (2011, methodology): “As in [6], [7], [27], we find it easier to study the algorithm in terms of a single virtual clock that ticks whenever any of the local Poisson clock ticks.”
Push-Sum Distributed Dual Averaging for convex optimization (2012, background): “Moreover, with a time-varying consensus protocol P (t), we may not even be able to specify the stationary distribution beyond its expectation and variance [9] or may only be able to achieve average consensus in expectation [10].”
Distributed Asynchronous Constrained Stochastic Optimization (2011, background): “One of the most important characteristics of the network utility maximization problem is the fact that the objective function to be minimized has a separable form.”