Distributed Average Consensus With Dithered Quantization

By Tuncer Can Aysal, Mark J. Coates, Michael G. Rabbat. 2008

320 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

Citations in the corpus

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, methodology): “The proposed schemes in (Carli et al., 2007; Nedi´c et al., 2008; Aysal et al., 2008; Carli et al., 2010b; Yuan et al., 2012) do only converge to a neighborhood (whose size depends on the compression accuracy) of the solution.”
Gossip consensus algorithms via quantized communication (2009, background): “(9) 2 Elsewhere in the literature [1,8,16] this quantizer has been introduced as a result of dithering, that is the addition of a small random noise before (deterministic) quantization.”
Distributed Average Consensus With Quantization Refinement (2013, background): “The authors in [8] propose a probabilistic quantization scheme that is shown to reach a consensus almost surely to a random variable whose expected value is equal to the desired average.”
Spatial anomaly detection in sensor networks using neighborhood information (2017, background): “However, to converge to a single global consensus, one has to account for the unreliability and limited bandwidth of wireless communications [37] .”
Nested Distributed Gradient Methods with Adaptive Quantized Communication (2019, methodology): “Common approaches include, but are not limited to, allowing the number of quantization levels to approach infinity [20]–[24], preserving the statistical properties of vectors with probabilistic quantization [15], [21], [22], using weighted averages of quantized consensus 2019 IEEE 58th Conference on”
Adaptation, Learning, and Optimization over Networks (2014, unknown): “, [13, 87, 204]), attenuation and fading effects.”
A Gossip-Based Algorithm for Discrete Consensus Over Heterogeneous Networks (2010, background): “This defines a new framework called quantized consensus [7]–[9], where the state only takes nonnegative integer values.”
A Method to Improve Consensus Averaging using Quantized ADMM (2019, background): “To reduce communication costs, quantized consensus algorithms have been proposed in [7]–[9], [11], [19] where exchanges between nodes are quantized.”
Stochastic consensus over noisy networks with Markovian and arbitrary switches (2010, methodology): “This divergence behavior is in sharp contrast to the convergence behavior observed in Aysal et al. (2008), where a fixed weight coefficient matrix and homogenous quantizers, which have the same set of quantization levels, are used at all nodes.”
Distributed averaging on digital erasure networks (2011, background): “In fact, some recent work has addressed the cases of quantized communication [2, 8, 13, 3, 15], or transmission affected by additive noise [11, 17, 12].”
Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures (2007, background): “Also, while the probabilistic consensus in [22], [26], [27] converges almo st surely to a quantized level, in our work, we show that dithered consensus converges a.”
Gossip Algorithms for Distributed Signal Processing (2010, background): “This modest change enables gossip to converge to a consensus almost surely, as shown in [76].”
Distributed Consensus With Limited Communication Data Rate (2011, background): “More relevant results with dithered quantizers can be found in [24] and [25].”
Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization (2018, background): “Distributed algorithms with random (dithered) quantization have been considered in [10] for solving network consensus problems, a special case of the problem considered in this article.”