  Federated edge learning (FEEL) has emerged as an effective approach to reduce
the large communication latency in Cloud-based machine learning solutions,
while preserving data privacy. Unfortunately, the learning performance of FEEL
may be compromised due to limited training data in a single edge cluster. In
this paper, we investigate a novel framework of FEEL, namely semi-decentralized
federated edge learning (SD-FEEL). By allowing model aggregation across
different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the
training latency, while improving the learning performance by accessing richer
training data from multiple edge clusters. A training algorithm for SD-FEEL
with three main procedures in each round is presented, including local model
updates, intra-cluster and inter-cluster model aggregations, which is proved to
converge on non-independent and identically distributed (non-IID) data. We also
characterize the interplay between the network topology of the edge servers and
the communication overhead of inter-cluster model aggregation on the training
performance. Experiment results corroborate our analysis and demonstrate the
effectiveness of SD-FFEL in achieving faster convergence than traditional
federated learning architectures. Besides, guidelines on choosing critical
hyper-parameters of the training algorithm are also provided.
