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.

提出了一种结合更新年龄（Age of Update）概念和数据 Shapley 指标的调度策略，以提高联邦学习的运行效率。该算法简单且通过模拟验证了其有效性。

信息价值与时序感知调度在联邦学习中的应用

Value of Information and Timing-aware Scheduling for Federated Learning

The emergence of large-scale wireless networks with partially-observable and
time-varying dynamics has imposed new challenges on the design of optimal
control policies. This paper studies efficient scheduling algorithms for
wireless networks subject to generalized interference constraint, where mean
arrival and mean service rates are unknown and non-stationary. This model
exemplifies realistic edge devices' characteristics of wireless communication
in modern networks. We propose a novel algorithm termed MW-UCB for generalized
wireless network scheduling, which is based on the Max-Weight policy and
leverages the Sliding-Window Upper-Confidence Bound to learn the channels'
statistics under non-stationarity. MW-UCB is provably throughput-optimal under
mild assumptions on the variability of mean service rates. Specifically, as
long as the total variation in mean service rates over any time period grows
sub-linearly in time, we show that MW-UCB can achieve the stability region
arbitrarily close to the stability region of the class of policies with full
knowledge of the channel statistics. Extensive simulations validate our
theoretical results and demonstrate the favorable performance of MW-UCB.

对于具有部分可观察和时变动力学的大规模无线网络，本文研究了一种面向广义干扰约束的无线网络的高效调度算法，其中平均到达率和平均服务率是未知的和非平稳的。我们提出了一种新颖的算法 MW-UCB 用于广义无线网络调度，它基于最大权重策略，并利用滑动窗口上限置信界来学习通道在非平稳情况下的统计特性。在平均服务率的可变性方面满足较弱的假设条件下，MW-UCB 在吞吐量上是最优的。具体而言，只要平均服务率在任何时间段内的总变化量在时间上增长的次线性，我们表明 MW-UCB 可以实现稳定区域任意靠近拥有完全通道统计信息的策略类的稳定区域。大量模拟验证了我们的理论结果，并展示了 MW-UCB 的良好性能。

学习未知统计量的非静态无线网络调度

Learning to Schedule in Non-Stationary Wireless Networks With Unknown  Statistics

Federated learning (FL) is a machine learning model that preserves data
privacy in the training process. Specifically, FL brings the model directly to
the user equipments (UEs) for local training, where an edge server periodically
collects the trained parameters to produce an improved model and sends it back
to the UEs. However, since communication usually occurs through a limited
spectrum, only a portion of the UEs can update their parameters upon each
global aggregation. As such, new scheduling algorithms have to be engineered to
facilitate the full implementation of FL. In this paper, based on a metric
termed the age of update (AoU), we propose a scheduling policy by jointly
accounting for the staleness of the received parameters and the instantaneous
channel qualities to improve the running efficiency of FL. The proposed
algorithm has low complexity and its effectiveness is demonstrated by Monte
Carol simulations.

本文基于更新时效度（AoU）提出了一种调度策略，该策略可以同时考虑接收参数的陈旧度和即时信道质量，以提高联邦学习的运行效率，并通过蒙特卡罗模拟验证了算法的有效性。