Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum allocation). However, real-world wireless networks are susceptible to temporal variations of wireless channel capacity and users' datasets. In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called $\mathscr{D}$-Events, and (M2) dynamic datasets of users. The latter is characterized by (M2-a) modeling the dynamics of user's dataset size via an ordinary differential equation and (M2-b) introducing dynamic model drift}, formulated via a partial differential inequality} drawing concrete analytical connections between the dynamics of users' datasets and FL accuracy. We then conduct FL orchestration under MSDs by introducing dynamic cooperative FL with dedicated MAC schedulers (DCLM), exploiting the unique features of open radio access network (O-RAN). DCLM proposes (i) a hierarchical device-to-device (D2D)-assisted model training, (ii) dynamic control decisions through dedicated O-RAN MAC schedulers, and (iii) asymmetric user selection. We provide extensive theoretical analysis to study the convergence of DCLM. We then optimize the degrees of freedom (e.g., user selection and spectrum allocation) in DCLM through a highly non-convex optimization problem. We develop a systematic approach to obtain the solution for this problem, opening the door to solving a broad variety of network-aware FL optimization problems. We show the efficiency of DCLM via numerical simulations and provide a series of future directions.

翻译：现有关于联邦学习的研究大多集中于针对网络静态快照的系统编排以及做出静态控制决策（例如频谱分配）。然而，现实世界的无线网络易受无线信道容量和用户数据集的时变特性影响。本文我们将多粒度系统动态纳入联邦学习中，包括：（M1）动态无线信道容量，通过一组称为$\mathscr{D}$-事件的离散时间事件来捕捉；（M2）用户的动态数据集。后者通过以下方式表征：（M2-a）通过常微分方程对用户数据集大小的动态变化进行建模，以及（M2-b）引入动态模型漂移，通过偏微分不等式进行公式化，从而在用户数据集动态与联邦学习精度之间建立具体的分析联系。随后，我们通过利用开放无线接入网络的独特特性，引入具有专用MAC调度器的动态协作联邦学习，在多粒度系统动态下进行联邦学习编排。DCLM提出了：（i）分层设备到设备辅助的模型训练，（ii）通过专用O-RAN MAC调度器实现动态控制决策，以及（iii）非对称用户选择。我们提供了广泛的理论分析来研究DCLM的收敛性。接着，我们通过一个高度非凸的优化问题来优化DCLM中的自由度（例如用户选择和频谱分配）。我们开发了一种系统性的方法来获取该问题的解，为解决广泛类型的网络感知联邦学习优化问题打开了大门。我们通过数值模拟展示了DCLM的效率，并提供了一系列未来研究方向。