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

人工智能(其子集被称为机器学习，ML)和无线网络(其最新一代是5G及以后)是为明天的世界铺平道路的两项颠覆性技术。随着世界从新冠肺炎疫情中走出来，这些技术的协同效应预计将使医疗保健和环境可持续发展等广泛的应用领域受益，这些领域吸引了越来越多的关注。尽管存在这种潜力，但这些应用程序使用的现有分布式ML框架在管理用于训练模型的数据时无法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。该项目旨在为训练数据的网络管理奠定理论基础，以应对上述挑战，并提高分布式ML模型和相应应用的性能。为此，它结合了监督和强化学习、深度神经网络和网络优化的工具。在正在进行的试验台和实验活动的基础上，实现了理论框架，并使用真实的无线试验台组件进行了试验。这个项目提出了一种新的分布式机器学习(ML)方法，它使网络边缘的设备能够彼此共享训练数据，作为包括收集、丢弃、缓存、处理和传输数据在内的整体五重数据管理策略的一部分。这些是优化数据管理的额外自由度，可以帮助提高ML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。具体地说，提出了以下三个相互依存的研究重点：(1)数据管理的优化框架，将注意力从“在哪里收集数据”转移到“在哪里处理数据”，以此方式促进对相关学习和联网问题的解决方案的共同设计；(2)通过了解现有数据的重要性并相应地优化其管理决策来扩展这些解决方法；以及(Iii)各种应用的试验台实施和试验，包括一个用于环境传感的应用，另一个用于网络切片，旨在增强现实和虚拟现实用于远程教育。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，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.