Network-based consensus averaging with general noisy channels

dc.creatorRajagopal, Ram
dc.creatorWainwright, Martin J.
dc.date2008-05-04
dc.date.accessioned2026-07-07T09:36:59Z
dc.date.available2026-07-07T09:36:59Z
dc.descriptionThis paper focuses on the consensus averaging problem on graphs under general noisy channels. We study a particular class of distributed consensus algorithms based on damped updates, and using the ordinary differential equation method, we prove that the updates converge almost surely to exact consensus for finite variance noise. Our analysis applies to various types of stochastic disturbances, including errors in parameters, transmission noise, and quantization noise. Under a suitable stability condition, we prove that the error is asymptotically Gaussian, and we show how the asymptotic covariance is specified by the graph Laplacian. For additive parameter noise, we show how the scaling of the asymptotic MSE is controlled by the spectral gap of the Laplacian.
dc.descriptionPresented in part at the Allerton Conference on Control, Computing, and Communication (September 2007). Appeared as Dept. of Statistics, Technical Report, UC Berkeley
dc.identifierhttps://arxiv.org/abs/0805.0438
dc.identifierhttp://arxiv.org/abs/0805.0438
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/160308
dc.subjectInformation Theory
dc.titleNetwork-based consensus averaging with general noisy channels
dc.typetext

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