Nassif, RoulaRichard, CedricFerrari, AndreSayed, Ali H.2017-12-192017-12-192017-12-19201510.1109/ICASSP.2015.7178625https://infoscience.epfl.ch/handle/20.500.14299/143399In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-square error criterion regularized by a term that promotes piecewise constant transitions in the parameter vector entries estimated by neighboring clusters. We provide some results on the mean and mean-square-error convergence. Simulations are conducted to illustrate the effectiveness of the strategy.text::conference output::conference proceedings::conference paper