  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.
