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.

翻译：本文处理联邦学习（FL）场景中的客户端选择问题，利用学习目标与参与者的本地激励来构建目标导向的通信问题。具体而言，我们引入参与者的风险厌恶特性，在依赖参数服务器（PS）反馈的同时，实现通信高效的设备端性能。客户端需根据自身内在激励（即该客户端参与后训练所得全局模型的价值）决定何时不参与FL的传输计划。低质量更新不仅会因增加通信成本而损害全局模型性能，还会将性能损失传播至其他参与设备。我们将本地更新的相关性视为开发本地传输策略的“语义信息”，即做出“不传输”的决策。设备利用关于参数服务器状态的反馈，评估每个聚合周期中对训练学习模型的贡献，从而降低占用连接数量。仿真结果验证了所提方法的有效性，与基线方法相比，通信链路利用率提升高达1.4倍。