Collaborative Research: CCSS: Hierarchical Federated Learning over Highly-Dense and Overlapping NextG Wireless Deployments: Orchestrating Resources for Performance

协作研究：CCSS：高密度和重叠的 NextG 无线部署的分层联合学习：编排资源以提高性能

• 批准号: 2319780
• 负责人: Jie Xu
• 金额: $ 22.5万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319780&HistoricalAwards=false
• 关键词: Collaborative; Research; CCSS; Hierarchical; Federated

Federated learning (FL) is a distributed framework proposed for training machine learning (ML) models on mobile devices in Next Generation (NextG) wireless communication systems. Hierarchical federated learning (HFL) is an architecture that shows promise in enabling FL over wireless networks. However, existing research on HFL falls short in effectively addressing the challenges posed by the NextG communication environment, such as high user and edge server density, diverse edge server deployments, and overlapping wireless coverage. To tackle these challenges, this project aims to investigate resource allocation problems in HFL, focusing on selecting mobile clients to participate in HFL, associating them with edge servers, and allocating sufficient bandwidth under these demanding conditions. The successful completion of the proposed framework has the potential in transforming the deployment and operation of NextG systems and will provide support for a wide range of ML-powered applications and services. The primary objective in designing the performance of HFL over wireless networks is to optimize the overall training time required for convergence. This can be achieved by minimizing the time duration of each HFL round through efficient allocation of wireless bandwidth to each client (the bandwidth allocation problem). However, due to high client density, limited wireless spectrum, and mobility, not all clients may be able to participate in every round. This leads to the need to determine which clients should participate in each round (the client selection problem) and which clients should be associated with which edge server given the overlapping wireless coverage and presence of multiple providers (the client association problem). To address these challenges, the project focuses on three key research areas: (i) designing short-term bandwidth allocation for HFL under highly dense and heterogeneous deployments, (ii) developing a long-term optimization framework to solve user selection and association for HFL, and (iii) improving HFL under the emerging scenario in which a mobile client can be associated with multiple edge servers.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.

联邦学习 (FL) 是一种分布式框架，旨在用于在下一代 (NextG) 无线通信系统中的移动设备上训练机器学习 (ML) 模型。分层联合学习 (HFL) 是一种有望在无线网络上实现 FL 的架构。然而，现有的 HFL 研究未能有效解决 NextG 通信环境带来的挑战，例如用户和边缘服务器密度高、边缘服务器部署多样化以及无线覆盖重叠等。为了应对这些挑战，该项目旨在研究HFL中的资源分配问题，重点是选择移动客户端参与HFL，将它们与边缘服务器关联，并在这些苛刻条件下分配足够的带宽。拟议框架的成功完成有可能改变 NextG 系统的部署和操作，并将为广泛的 ML 支持的应用程序和服务提供支持。设计无线网络上 HFL 性能的主要目标是优化收敛所需的总体训练时间。这可以通过向每个客户端有效分配无线带宽（带宽分配问题）来最小化每个 HFL 轮的持续时间来实现。然而，由于客户端密度高、无线频谱有限和移动性，并非所有客户端都能够参与每一轮。这导致需要确定哪些客户端应该参与每一轮（客户端选择问题），以及哪些客户端应该与哪个边缘服务器关联（考虑到重叠的无线覆盖范围和多个提供商的存在）（客户端关联问题）。为了应对这些挑战，该项目重点关注三个关键研究领域：(i) 设计高密度和异构部署下 HFL 的短期带宽分配，(ii) 开发长期优化框架来解决 HFL 的用户选择和关联问题，以及 (iii) 在移动客户端可以与多个边缘服务器关联的新兴场景下改进 HFL。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。