HFEL：联合边缘关联和资源分配用于经济高效的分层联邦边缘学习
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
摘要：
联邦学习（FL）已经被提出作为一种有吸引力的方法来处理移动设备的数据隐私问题，与在远程云处的具有原始用户数据上传的传统机器学习相比。通过利用边缘服务器作为中介来执行邻近的部分模型聚合并减轻核心网络传输开销，它在低延迟和节能的FL中具有很大的潜力。因此，我们引入了一种新的分层联邦边缘学习（HFEL）框架，其中模型聚合部分从云中迁移到边缘服务器。在HFEL框架下，提出了一个计算与通信联合的资源分配和边关联问题，以实现全局代价最小化.为了解决这个问题，我们提出了一个高效的资源调度算法在HFEL框架。它可以分解为两个子问题：给定每个边缘服务器的一组预定设备的\emph{resource allocation}和跨所有边缘服务器的设备用户的\emph{edge association}。利用单个边缘服务器下的一组设备的凸资源分配子问题的最优策略，通过迭代的全局代价降低调整过程，可以得到一个有效的边缘关联策略，并证明该策略收敛到一个稳定的系统点.广泛的性能评估表明，我们的HFEL框架在全局成本节省方面优于所提出的基准，并且与传统的联邦学习相比，实现了更好的训练性能。
英文摘要：
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.
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