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.

联合学习（FL）是在下一代（NextG）无线通信系统中的移动设备上提出的用于培训机器学习（ML）模型的分布式框架。分层联合学习（HFL）是一种体系结构，它显示出使FL超过无线网络的希望。但是，对HFL的现有研究有效地解决了NextG通信环境所带来的挑战，例如高用户和边缘服务器密度，各种边缘服务器部署以及重叠的无线覆盖范围。为了应对这些挑战，该项目旨在调查HFL中的资源分配问题，专注于选择移动客户参与HFL，将其与边缘服务器相关联，并在这些苛刻的条件下分配足够的带宽。所提出的框架的成功完成有可能改变NextG系统的部署和操作，并将为各种ML供电的应用程序和服务提供支持。设计HFL在无线网络上的性能的主要目的是优化收敛所需的整体训练时间。通过将无线带宽分配给每个客户端（带宽分配问题），可以通过将每个HFL的时间持续时间最小化来实现。但是，由于客户密度较高，无线频谱有限和机动性，并非所有客户都可以参与每个回合。这导致需要确定哪些客户应参与每个回合（客户选择问题），以及哪些客户应与哪个Edge Server相关联，给出了重叠的无线覆盖范围和多个提供商的存在（客户关联问题）。为了应对这些挑战，该项目着重于三个关键研究领域：（i）在高度密集和异质部署下设计HFL的短期带宽分配，（ii）开发一个长期优化框架，以解决HFL的用户选择和（III）在新兴客户群体下与多个Edders的统计相关联的HFL的使用者，并将其与多个移动客户的统计相关联。认为值得通过基金会的智力优点和更广泛影响的评论标准来评估值得支持。