Distributed Average Consensus using Probabilistic Quantization

By Tuncer Can Aysal, Mark J. Coates, Michael G. Rabbat. 2007

133 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

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Citations in the corpus

Robust and Communication-Efficient Collaborative Learning (2019, methodology): “In the decentralized setting, quantization methods were proposed in different convex optimization contexts with nonvanishing errors (Yuksel and Basar, 2003; Rabbat and Nowak, 2005; Kashyap et al., 2006; El Chamie et al., 2016; Aysal et al., 2007; Nedic et al., 2008).”
Gossip consensus algorithms via quantized communication (2009, background): “≤ E [ 1 4 ]”
On the Convergence Time of Asynchronous Distributed Quantized Averaging Algorithms (2010, background): “Prior work on distributed quantized averaging over fixed graphs includes [1], [6], [7], [12].”
Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures (2007, background): “Di tributed consensus with quantized transmission has bee n studied recently in [20], [21], [22], [23] with respect to time-inva ri nt (fixed) topologies.”
Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise (2007, unknown): “More precisely, we assume that there exists a two-parameter random field,{L(i,x)}i≥0,x∈RN×1 of Laplacian matrices such that E[L(i,x)] = L, ∀i,x (12) and λ2(L) > 0.”
Quantized Decentralized Consensus Optimization (2018, background): “However, randomized quantization schemes are shown to achieve the average of initials, in expectation [28].”
Quantized average consensus via dynamic coding/decoding schemes (2008, methodology): “The authors in [6] adopt the probabilistic quantization (PQ) scheme to quantize the information before transmitting to the”
Distributed Average Consensus With Dithered Quantization (2008, methodology): “We simulate a network with nodes randomly dispersed on the unit square [0,1] [0,1], connecting two nodes by an edge if the distance between them is less than the connectivity radius, i.”
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication (2008, methodology): “The LU algorithm can be regarded as a generalization of consensus algorithms (se e, for example, [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]), the latter being a specific cas e of theLU with no innovations.”
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.”
Broadcast Gossip Algorithms for Consensus (2009, background): “Moreover, we note that we assume that the messages involve real numbers; the effects of message quantization in gossip and consensus algorithms, is an active area of research [17], [19], [20], [23]–[27].”
Gossip Algorithms for Distributed Signal Processing (2010, background): “To overcome the fact that not all nodes end up having the same quantized value, a simple variant on the quantized consensus problem that guarantees almost sure convergence to a unique consensus point was proposed concurrently in [74] and [75].”
Design and analysis of distributed averaging with quantized communication (2014, background): “The probabilistic quantizer in [2] ensures almost sure consensus at a common but random quantization level for fixed (strongly connected) directed graphs; although the expectation of the consensus value equals the desired average, the deviation of the consensus value from the desired average is not ”