  Limited radio frequency (RF) resources restrict the number of users that can
participate in federated learning (FL) thus affecting FL convergence speed and
performance. In this paper, we first introduce visible light communication
(VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication
system, in which each indoor user can use both VLC and RF to transmit its FL
model parameters. Then, the problem of user selection and bandwidth allocation
is studied for FL implemented over a hybrid VLC/RF system aiming to optimize
the FL performance. The problem is first separated into two subproblems. The
first subproblem is a user selection problem with a given bandwidth allocation,
which is solved by a traversal algorithm. The second subproblem is a bandwidth
allocation problem with a given user selection, which is solved by a numerical
method. The final user selection and bandwidth allocation are obtained by
iteratively solving these two subproblems. Simulation results show that the
proposed FL algorithm that efficiently uses VLC and RF for FL model
transmission can improve the prediction accuracy by up to 10% compared with a
conventional FL system using only RF.
