  Federated learning (FL) has been considered as a promising learning framework
for future machine learning systems due to its privacy preservation and
communication efficiency. In beyond-5G/6G systems, it is likely to have
multiple FL groups with different learning purposes. This scenario leads to a
question: How does a wireless network support multiple FL groups? As an answer,
we first propose to use a cell-free massive multiple-input multiple-output
(MIMO) network to guarantee the stable operation of multiple FL processes by
letting the iterations of these FL processes be executed together within a
large-scale coherence time. We then develop a novel scheme that asynchronously
executes the iterations of FL processes under multicasting downlink and
conventional uplink transmission protocols. Finally, we propose a
simple/low-complexity resource allocation algorithm which optimally chooses the
power and computation resources to minimize the execution time of each
iteration of each FL process.
