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This paper considers multi-agent distributed optimization with quantized communication which is needed when inter-agent communications are subject to finite capacity and other practical constraints. To minimize the global objective formed by a sum of local convex functions, we develop a quantized distributed algorithm based on the alternating direction method of multipliers (ADMM). Under certain convexity...
A guiding principle for data reduction in statistical inference is the sufficiency principle. This paper extends the classical sufficiency principle to decentralized inference, i.e., data reduction needs to be achieved in a decentralized manner. We examine the notions of local and global sufficient statistics and the relationship between the two for decentralized inference under different observation...
The sufficiency principle is the guiding principle for data reduction in statistical inference. There has been recent effort in developing the sufficiency principle for decentralized inference with a particular emphasis on the relationship between global sufficiency and local sufficiency. This paper studies the sufficiency based data reduction in tandem fusion systems when quantization is needed....
In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles...
We consider the problem of distributed test of statistical independence under communication constraints. While independence test is frequently encountered in various applications, distributed independence test is particularly useful for events detection in sensor networks: data correlation often occurs among sensor observations in the presence of a target. Focusing on the Gaussian case because of...
Distributed detection with conditionally independent observations at local sensors is well understood. The problem becomes significantly more complicated when dependence is present among sensor observations. In this paper, we attempt to make progress in our understanding of the dependent observation case. Toward this end, we present a new hierarchical model by introducing a hidden or latent variable;...
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