发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个统一框架，联合优化OTA联邦学习中的感知、计算与通信决策，以平衡能量、延迟和学习质量，并通过交替优化算法求解，实验验证其优势。
AI 中文摘要
大多数联邦学习（FL）研究隐含地假设训练数据在参与的边缘设备上随时可用。在实践中，这些数据必须首先通过具有潜在不同测量模态的异构传感器获取，而这一获取过程的质量和成本会直接影响学习性能。这种依赖性在感知、计算和通信之间形成了耦合关系，因此需要一个能够捕捉它们对资源效率和学习准确性的联合影响的系统模型。本文针对一种空中（OTA）联邦学习系统开发了这样的统一处理方法，其中每个设备配置其感知模态、功率、分辨率和样本大小，聚合在服务器端通过OTA进行。我们推导了感知噪声、能耗、延迟和OTA聚合失真的模型，并建立了一个非凸收敛界，量化了这些因素如何影响全局模型轨迹。在此分析的指导下，我们制定了一个每轮优化，在物理设备约束下平衡能量、延迟和学习质量。由此产生的混合优化通过一种交替过程解决，该过程对连续变量采用闭式和一维更新，并进行轻量级离散搜索。数值实验证实了联合优化感知、计算和通信决策的优势。
英文摘要
Most federated learning (FL) studies implicitly assume that training data is readily available at participating edge devices. In practice, this data must first be acquired through heterogeneous sensors with potentially different measurement modalities, and the quality and cost of this acquisition process can directly affect the learning performance. This dependence creates a coupled relationship among sensing, computation, and communication, necessitating a system model that captures their joint effect on resource efficiency and learning accuracy. This paper develops such a unified treatment for an over-the-air (OTA) FL system in which each device configures its sensing modality, power, resolution, and sample size, with aggregations conducted OTA at the server. We derive models for sensing noise, energy consumption, latency, and OTA aggregation distortion, and establish a non-convex convergence bound quantifying how these factors influence the global model trajectory. Guided by this analysis, we formulate a per-round optimization that balances energy, latency, and learning quality under physical device constraints. The resulting mixed optimization is addressed through an alternating procedure with closed-form and one-dimensional updates for continuous variables and a lightweight discrete search. Numerical experiments corroborate the advantage of jointly optimizing sensing, computation, and communication decisions.
