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