Federated learning has recently appeared as a promising machine learning approach which respects the privacy of the data We envision novel federated learning applications in which any node can participate in the provision of data and in the training of machine learning models For this to happen federated learning building blocks must be extended and evaluated These building blocks will enable the autonomy of the participants for taking self-determined decisions facilitate the ownership of machine learning models and data and enable decentralized governance of the data This scenario will be realistic in the near future as edge nodes of a federated learning network will receive an ever increasing amount of data coming from more and more nearby sensors The experiment obtains feedback from a prototype experimentally evaluated in a testbed deployed within the GuifiSants wireless city mesh network