Towfic, Zaid J.Chen, JianshuSayed, Ali H.2017-12-192017-12-192017-12-19201110.1109/MLSP.2011.6064578https://infoscience.epfl.ch/handle/20.500.14299/143155In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that is collected from across the network. In this paper, we develop a decentralized adaptive strategy for information processing and apply it to the task of estimating the parameters of a Gaussian-mixture-model (GMM). The proposed technique employs adaptive diffusion algorithms that enable adaptation, learning, and cooperation at local levels. The simulation results illustrate how the proposed technique outperforms non-collaborative learning and is competitive against centralized solutions.text::conference output::conference proceedings::conference paper