In 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.
Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux
Petr Motlicek, Mathew Magimai Doss, Julian David Fritsch, Subrahmanya Pavankumar Dubagunta
Haomin Sun, Michele Marin, Javier García Hernández, Mikhail Maslov