  We treat the problem of client selection in a Federated Learning (FL) setup,
where the learning objective and the local incentives of the participants are
used to formulate a goal-oriented communication problem. Specifically, we
incorporate the risk-averse nature of participants and obtain a
communication-efficient on-device performance, while relying on feedback from
the Parameter Server (\texttt{PS}). A client has to decide its transmission
plan on when not to participate in FL. This is based on its intrinsic
incentive, which is the value of the trained global model upon participation by
this client. Poor updates not only plunge the performance of the global model
with added communication cost but also propagate the loss in performance on
other participating devices. We cast the relevance of local updates as
\emph{semantic information} for developing local transmission strategies, i.e.,
making a decision on when to ``not transmit". The devices use feedback about
the state of the PS and evaluate their contributions in training the learning
model in each aggregation period, which eventually lowers the number of
occupied connections. Simulation results validate the efficacy of our proposed
approach, with up to $1.4\times$ gain in communication links utilization as
compared with the baselines.
