NSF-AoF: CNS Core Small: Lean-NextG: Learning to Network the Edge in Next Generation Wireless Networks

人工智能（及其子集被称为机器学习，ML）和无线网络（最新一代，5G及以后）是为未来世界铺平道路的两项颠覆性技术。这些技术的协同作用预计将惠及广泛的应用领域，如医疗保健和环境可持续性，随着世界从COVID-19大流行中走出来，这些领域越来越受到关注。尽管有这种潜力，这些应用程序所使用的分布式机器学习的现有框架在管理用于训练模型的数据时，未能充分利用不同类型的网络资源。此外，明智地选择重要的数据子集并在最需要它们的地方提供它们仍然是一个悬而未决的问题。本项目旨在为训练数据的网络管理提供理论基础，以解决上述挑战，并提高分布式ML模型和相应应用的性能。为此，它结合了来自监督和强化学习、深度神经网络和网络优化的工具。基于正在进行的试验台和实验活动，理论框架被实现并在实际无线试验台组件上进行实验。pi还将针对K-12学生开展各种外展活动。该项目提出了一种新的分布式机器学习（ML）方法，该方法与当前的联邦学习（FL）实践有很大不同，它使网络边缘的设备能够相互共享训练数据，作为整体五重数据管理策略的一部分，包括收集、丢弃、缓存、处理和传输数据。这些是优化数据管理的额外自由度，可以帮助提高机器学习模型和相应应用程序的性能，同时探索边缘网络中不同类型资源之间的整个权衡范围。具体而言，提出了以下三个相互依存的研究重点：(i)数据管理的优化框架，将注意力从“数据在哪里收集”转移到“数据在哪里处理”，从而促进相关学习和网络问题解决方案的共同设计；（ii）通过了解可用数据的重要性并相应地优化其管理决策，扩展这些解决方案方法；（iii）一个测试平台的实施和各种应用的实验，包括一个用于环境传感和另一个用于网络切片，旨在在远程教育中使用增强现实和虚拟现实。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial Intelligence (with its subset known as Machine Learning, ML) and wireless networking (in its most recent generation, 5G, and beyond) are two disruptive technologies paving the way for the world of tomorrow. The synergy of these technologies is expected to benefit a wide range of application domains, such as healthcare and environmental sustainability, that attract increasing attention as the world moves on from the COVID-19 pandemic. In spite of this potential, the existing frameworks for distributed ML used by these applications fail to fully utilize the different types of network resources when managing the data they use for training models. Furthermore, judiciously selecting important subsets of data and making them available where they are needed the most continues to be open an problem. This project aims to develop the theoretical foundations for the in-network management of the training data to address the above challenges and enhance the performance of the distributed ML models and corresponding applications. Towards this, it combines tools from supervised and reinforcement learning, deep neural networks, and network optimization. Building on ongoing testbed and experimentation activities, the theoretical framework is implemented and experimented with real-life wireless testbed components. The PIs will also engage in various outreach activities targeting K-12 students.This project proposes a new distributed Machine Learning (ML) methodology that departs significantly from the current practice of Federated Learning (FL) by enabling devices at the network edge to share training data to each other as part of an overall fivefold data management strategy that includes collecting, discarding, caching, processing and transferring of data. These are additional degrees of freedom in optimizing data management that can help improving the performance of the ML models and the corresponding applications while at the same time exploring the entire gamut of tradeoffs between the different types of resources in the edge network. Specifically, the following three interdependent research thrusts are proposed: (i) an optimization framework for data management that shifts the attention from "where the data is collected"' to "where the data is processed" this way facilitating the co-design of solutions to related learning and networking problems, (ii) an extension of these solution methods by learning the importance of the available data and optimizing their management decisions accordingly, and (iii) a testbed implementation and experimentation with various applications including one for environmental sensing and another for network slicing aimed at augmented reality and virtual reality used in tele-education.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.