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We treat the problem of client selection in a Federated Learning (FL) setup,where the learning objective and the local incentives of the participants areused to formulate a goal-oriented communication problem. Specifically, weincorporate the risk-averse nature of participants and obtain acommunication-efficient on-device performance, while relying on feedback fromthe Parameter Server (\texttt{PS}). A client has to decide its transmissionplan on when not to participate in FL. This is based on its intrinsicincentive, which is the value of the trained global model upon participation bythis client. Poor updates not only plunge the performance of the global modelwith added communication cost but also propagate the loss in performance onother 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 aboutthe state of the PS and evaluate their contributions in training the learningmodel in each aggregation period, which eventually lowers the number ofoccupied connections. Simulation results validate the efficacy of our proposedapproach, with up to $1.4\times$ gain in communication links utilization ascompared with the baselines.

Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation