Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.

翻译：数据作为人工智能发展的驱动力具有显著价值。然而，保护终端用户设备产生的数据隐私已成为关键问题。联邦学习通过训练过程中保护数据隐私提供了解决方案：由接入点将模型直接部署到用户设备进行本地训练，该接入点定期聚合经训练的参数以优化模型并将其回传至用户设备。但由于通信约束，每次全局聚合时仅有部分用户设备能更新参数。因此，开发创新调度算法对实现联邦学习完整部署并加速其收敛至关重要。本文提出一种融合更新时效概念与数据夏普利值的调度策略，该策略综合考量各数据源所接收参数更新的新鲜度、价值以及实时信道条件，以提升联邦学习的运行效率。所提算法结构简洁，并通过仿真验证了其有效性。