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

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.