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)作为一种保护隐私的范例,吸引了人们的极大关注,因为这是一种保护隐私的模式,可以有效地将分布在网络边缘的数据纳入网络边缘,用于培训深层学习模式;然而,单一边缘服务器的覆盖范围有限,导致参与的客户节点数量不足,这可能会损害学习绩效;在本文件中,我们调查一种新的感觉框架,即半分散化联邦边缘学习(SD-FEEL),利用多个边缘服务器集体协调大量客户节点;通过利用边缘服务器之间的低延迟通信,有效共享模型共享,SD-FEEL可以纳入更多的培训数据,同时与传统的联邦化学习相比,保持更低的延迟。我们用三个主要步骤详细说明了SD-FEL的培训算法,包括当地模型更新、集群内和集群间模型组合。这一算法的趋同性,证明不依赖和同样分布的(非IID)数据,这也有助于揭示关键参数对培训效率的影响,并提供实用的设计准则。 同时,边缘装置的高度趋同性趋同性(SDFE-L)分析,这会使SD-LA-LA-SDLAGALAG的稳定性产生一种稳定速度。</s>