Federated Learning is a modern decentralized machine learning technique where
user equipments perform machine learning tasks locally and then upload the
model parameters to a central server. In this paper, we consider a 3-layer
hierarchical federated learning system which involves model parameter exchanges
between the cloud and edge servers, and the edge servers and user equipment. In
a hierarchical federated learning model, delay in communication and computation
of model parameters has a great impact on achieving a predefined global model
accuracy. Therefore, we formulate a joint learning and communication
optimization problem to minimize total model parameter communication and
computation delay, by optimizing local iteration counts and edge iteration
counts. To solve the problem, an iterative algorithm is proposed. After that, a
time-minimized UE-to-edge association algorithm is presented where the maximum
latency of the system is reduced. Simulation results show that the global model
converges faster under optimal edge server and local iteration counts. The
hierarchical federated learning latency is minimized with the proposed
UE-to-edge association strategy.

本文提出了一种层次化联邦学习系统，结合联邦学习中的通信优化问题，通过优化本地迭代计数和边缘迭代计数，减少模型参数通信和计算延迟，并提出一种时限最小化的 UE-to-edge 关联算法。仿真结果表明，最优的边缘服务器和本地迭代计数可以使全局模型更快地收敛，利用 UE-to-edge 关联的策略可以最小化层次联邦学习延迟。