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The proliferation of machine learning (ML) applications has lately witnessed a considerable shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to typical Centralized learning (CL) whereby the involved (large amounts of) training data are centrally gathered to train models, the load of training tasks is distributed across a set of capable mobile learners at the expense of their own energy. The idea of Federated learning (FL) has emerged as a privacy-preserving mechanism suggesting that the ML model parameters rather than data, are sent over the network to a central point of aggregation. However, when relaxing the privacy concerns, the debate strongly relates to the available network resources. Interestingly, the sofar theoretical or even experimental comparison of the two approaches overlooks network conditions and remains of low realism. In this work we rely on past …