Collaborative Research: CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization

在人工智能应用需求增长的推动下，过去十年见证了协作网络物理学习系统(CCPLS)的出现。CCPL通过网络物理系统(CPS)设备之间的协调执行分布式、基于学习的处理任务，并预计在未来几年提供跨商业和国防部门的关键功能。然而，CCPLS生成的数据往往是大规模、高维、异质、时变的，这给智能建模带来了严峻的挑战。与此同时，无人驾驶车辆，特别是无人驾驶飞行器(UAV)，在CCPLS中显示出扩大信息共享的希望，特别是在农村地区等服务不足的地区。该项目的创新之处在于为无人机-CCPLS集成建立了一个具体的基础，该基础统一了相关的学习、网络和通信设计方面，围绕适当的情报指标。该项目的影响是开发用于智能农业任务的无人机辅助CCPLS，以及推进为CCPLS量身定做的无人机和其他无人驾驶车辆的制造。项目成果将通过发布开放源码软件、研究视频和教程来传播。调查人员将进一步参与课程开发、多样性和外展活动，包括指导本科生研究人员。研究调查围绕三个相互关联的推进体展开。推力1号为CCPLS开发了一个新的无人机辅助智能框架，并引入了一种精确的、面向任务的数据动力学和异构性概念。此外，这一努力还开发了一个旨在预测性地估计数据动态速度的“以学换学”框架。推力2号研究了通过无人机轨迹和无人机到CPS网络建立的联合设计来联合优化资源利用和情报质量的方法。推力1中的数据动力学框架通过在线、网络感知的顺序决策框架集成到此设计中。最后，推力3开发了基于学习感知的上行链路和下行链路无线波束形成器和空中聚合方法的CCPLS通信协议。这些协议是为无人机辅助学习系统的特定需求量身定做的，例如，在无人机到无人机和无人机到接入点的通信链路上传输噪音信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Propelled by the growth in demand for artificial intelligence-enabled applications, the past decade has witnessed the emergence of Collaborative Cyber-Physical Learning Systems (CCPLS). CCPLS carry out distributed, learning-based processing tasks through coordination among Cyber-Physical System (CPS) devices, and are envisioned to provide critical functionality across the commercial and defense sectors in the next several years. However, the data generated by CCPLS is often large-scale, high-dimensional, heterogeneous, and time-varying, which poses critical challenges for intelligence modeling. Concurrently, unmanned vehicles, in particular Unmanned Aerial Vehicles (UAVs), have shown promise of scaling up information-sharing in CCPLS, especially in under-served regions such as rural areas. The project's novelties are in establishing a concrete foundation for UAV-CCPLS integration that unifies the associated learning, networking, and communication design aspects around appropriate intelligence metrics. The project's impacts are the development of UAV-assisted CCPLS for smart agriculture tasks, as well as advancing the manufacturing of UAVs and other unmanned vehicles tailored for CCPLS. Project outcomes will be disseminated by releasing open-source software and research videos and tutorials. The investigators will further engage in Curriculum development, diversity, and outreach activities including mentoring undergraduate researchers. Research investigations center around three interconnected thrusts. Thrust 1 develops a novel UAV-assisted intelligence framework for CCPLS and introduces a precise, task-oriented notion of data dynamics and heterogeneity. Additionally, this thrust develops a `learning for learning’ framework that aims to predictively estimate the rate of data dynamics. Thrust 2 investigates methodologies for jointly optimizing resource utilization and intelligence quality through co-design of UAV trajectories and UAV-to-CPS network establishment. The data dynamics framework from Thrust 1 is integrated into this design through an online, network-aware sequential decision-making framework. Finally, Thrust 3 develops CCPLS communication protocols based on learning-aware uplink and downlink wireless beamformers and over-the-air aggregation methods. These protocols are tailored to the specific needs of the UAV-assisted learning systems, e.g., the transmission of noisy information over UAV-to-UAV and UAV-to-access point communication links.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.