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We consider the problem of minimizing the total energy consumption due to the computation and communication tasks of federated learning (FL) under bandwidth and latency constraints. To avoid channel state information (CSI) feedback to the transmitter, we adopt outage probability as an additional constraint in the energy minimization problem. First, we define a feasibility metric based on the system design parameters to exclude slow clients (stragglers). Then, we propose a novel client selection algorithm, after excluding stragglers, based on dividing the remaining clients into clusters, where clients within the same cluster collaborate in a communication round to train their local models. For each communication round, one client cluster is selected in a round-robin fashion. Furthermore, we formulate and solve a resource allocation problem to optimize the transmit power, clock frequency, allocated bandwidth, and …