Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep learning models. Nevertheless, the limited coverage of a single edge server results in an insufficient number of participated client nodes, which may impair the learning performance. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL), where multiple edge servers are employed to collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning. We detail the training algorithm for SD-FEEL with three main steps, including local model update, intra-cluster, and inter-cluster model aggregations. The convergence of this algorithm is proved on non-independent and identically distributed (non-IID) data, which also helps to reveal the effects of key parameters on the training efficiency and provides practical design guidelines. Meanwhile, the heterogeneity of edge devices may cause the straggler effect and deteriorate the convergence speed of SD-FEEL. To resolve this issue, we propose an asynchronous training algorithm with a staleness-aware aggregation scheme for SD-FEEL, of which, the convergence performance is also analyzed. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithms for SD-FEEL and corroborate our analysis.

翻译：联邦边缘学习（FEEL）作为一种隐私保护范式，通过有效整合网络边缘的分布式数据以训练深度学习模型而备受关注。然而，单个边缘服务器的覆盖范围有限，导致参与的客户端节点数量不足，这可能削弱学习性能。本文研究了一种新型FEEL框架，即半去中心化联邦边缘学习（SD-FEEL），该框架利用多个边缘服务器协同协调大量客户端节点。通过利用边缘服务器间的低延迟通信实现高效模型共享，SD-FEEL可整合更多训练数据，同时相比传统联邦学习享有更低的延迟。我们详细阐述了包含本地模型更新、集群内聚合和集群间聚合三个主要步骤的SD-FEEL训练算法。该算法在非独立同分布（non-IID）数据上的收敛性已获证明，这有助于揭示关键参数对训练效率的影响，并为实际设计提供指导准则。此外，边缘设备的异构性可能导致落后节点效应，降低SD-FEEL的收敛速度。为解决该问题，我们提出了一种结合延迟感知聚合方案的SD-FEEL异步训练算法，并分析了其收敛性能。仿真结果验证了所提SD-FEEL算法的有效性和高效性，并佐证了我们的理论分析。