Federated Learning (FL) has been proposed as an appealing approach to handle
data privacy issue of mobile devices compared to conventional machine learning
at the remote cloud with raw user data uploading. By leveraging edge servers as
intermediaries to perform partial model aggregation in proximity and relieve
core network transmission overhead, it enables great potentials in low-latency
and energy-efficient FL. Hence we introduce a novel Hierarchical Federated Edge
Learning (HFEL) framework in which model aggregation is partially migrated to
edge servers from the cloud. We further formulate a joint computation and
communication resource allocation and edge association problem for device users
under HFEL framework to achieve global cost minimization. To solve the problem,
we propose an efficient resource scheduling algorithm in the HFEL framework. It
can be decomposed into two subproblems: \emph{resource allocation} given a
scheduled set of devices for each edge server and \emph{edge association} of
device users across all the edge servers. With the optimal policy of the convex
resource allocation subproblem for a set of devices under a single edge server,
an efficient edge association strategy can be achieved through iterative global
cost reduction adjustment process, which is shown to converge to a stable
system point. Extensive performance evaluations demonstrate that our HFEL
framework outperforms the proposed benchmarks in global cost saving and
achieves better training performance compared to conventional federated
learning.

介绍了一种新的 Hierarchical Federated Edge Learning（HFEL）框架，以及在此框架下计算和通信资源分配以及设备用户的边缘关联问题的解决方案，同时提出了一种有效的资源调度算法。经过广泛的性能评估，表明 HFEL 框架优于传统联邦学习的基准，并且可以实现更好的训练性能和全球成本节约。