With its privacy preservation and communication efficiency, federated\nlearning (FL) has emerged as a learning framework that suits beyond 5G and\ntowards 6G systems. This work looks into a future scenario in which there are\nmultiple groups with different learning purposes and participating in different\nFL processes. We give energy-efficient solutions to demonstrate that this\nscenario can be realistic. First, to ensure a stable operation of multiple FL\nprocesses over wireless channels, we propose to use a massive multiple-input\nmultiple-output network to support the local and global FL training updates,\nand let the iterations of these FL processes be executed within the same\nlarge-scale coherence time. Then, we develop asynchronous and synchronous\ntransmission protocols where these iterations are asynchronously and\nsynchronously executed, respectively, using the downlink unicasting and\nconventional uplink transmission schemes. Zero-forcing processing is utilized\nfor both uplink and downlink transmissions. Finally, we propose an algorithm\nthat optimally allocates power and computation resources to save energy at both\nbase station and user sides, while guaranteeing a given maximum execution time\nthreshold of each FL iteration. Compared to the baseline schemes, the proposed\nalgorithm significantly reduces the energy consumption, especially when the\nnumber of base station antennas is large.\n