  This paper proposes a novel scheme for cell-free massive multiple-input
multiple-output (CFmMIMO) networks to support any federated learning (FL)
framework. This scheme allows each instead of all the iterations of the FL
framework to happen in a large-scale coherence time to guarantee a stable
operation of an FL process. To show how to optimize the FL performance using
this proposed scheme, we consider an existing FL framework as an example and
target FL training time minimization for this framework. An optimization
problem is then formulated to jointly optimize the local accuracy, transmit
power, data rate, and users' processing frequency. This mixed-timescale
stochastic nonconvex problem captures the complex interactions among the
training time, and transmission and computation of training updates of one FL
process. By employing the online successive convex approximation approach, we
develop a new algorithm to solve the formulated problem with proven convergence
to the neighbourhood of its stationary points. Our numerical results confirm
that the presented joint design reduces the training time by up to $55\%$ over
baseline approaches. They also show that CFmMIMO here requires the lowest
training time for FL processes compared with cell-free time-division multiple
access massive MIMO and collocated massive MIMO.
