User Assignment and Resource Allocation for Hierarchical Federated
  Learning over Wireless Networks
Tinghao Zhang Kwok-Yan Lam Jun Zhao
The large population of wireless users is a key driver of data-crowdsourced
Machine Learning (ML). However, data privacy remains a significant concern.
Federated Learning (FL) encourages data sharing in ML without requiring data to
leave users' devices but imposes heavy computation and communications overheads
on mobile devices. Hierarchical FL (HFL) alleviates this problem by performing
partial model aggregation at edge servers. HFL can effectively reduce energy
consumption and latency through effective resource allocation and appropriate
user assignment. Nevertheless, resource allocation in HFL involves optimizing
multiple variables, and the objective function should consider both energy
consumption and latency, making the development of resource allocation
algorithms very complicated. Moreover, it is challenging to perform user
assignment, which is a combinatorial optimization problem in a large search
space. This article proposes a spectrum resource optimization algorithm (SROA)
and a two-stage iterative algorithm (TSIA) for HFL. Given an arbitrary user
assignment pattern, SROA optimizes CPU frequency, transmit power, and bandwidth
to minimize system cost. TSIA aims to find a user assignment pattern that
considerably reduces the total system cost. Experimental results demonstrate
the superiority of the proposed HFL framework over existing studies in energy
and latency reduction.