CEDRO falls into the broad theme of performing decentralized inference (stochastic optimization, estimation, and learning) over graphs. It notably recognizes the increasing ability of many emerging technologies to collect data in a decentralized and streamed manner. Therefore, the focus is on designing decentralized approaches where devices are collecting data in a continuous manner. The project also recognizes that modern machine learning applications (where tremendous volumes of training data are generated continuously by a massive number of heterogeneous devices) have several key properties that differentiate them from standard distributed inference applications. Particular focus will be given to developing and studying approaches for decentralized learning in statistical heterogeneous (multitask) settings in the presence of limited communication resources and heterogeneous system devices. The project emphasis will specifically be on illustrating the interest of the proposed approaches in machine learning frameworks using publicly available datasets.