Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur Graph Search.
Adaptive networks consist of a collection of nodes that interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In this work, we compare the performance of two distributed estimation strategies: diffusion and consensus. Diffusion strategies allow information to diffuse more thoroughly through the network. The analysis in the paper confirms that this property has a favorable effect on the evolution of the network: diffusion networks reach lower mean-square deviation than consensus networks, and their mean-square stability is insensitive to the choice of the combination weights. In contrast, consensus networks can become unstable even if all the individual nodes are mean-square stable; this does not occur for diffusion networks: stability of the individual nodes ensures stability of the diffusion network irrespective of the topology.
Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito