Distributed Estimation and Optimization in Asynchronous Networks

This thesis addresses the distributed estimation and optimization of a global value of interest over a network using only local and asynchronous (sometimes wireless) communications. Motivated by many different applications ranging from cloud computing to wireless sensor networks via machine learning, we design new algorithms and theoretically study three problems of very different nature : the propagation of the maximal initial value, the estimation of their average and finally distributed optimization.