  Federated learning (FL) is a promising paradigm that enables collaboratively
learning a shared model across massive clients while keeping the training data
locally. However, for many existing FL systems, clients need to frequently
exchange model parameters of large data size with the remote cloud server
directly via wide-area networks (WAN), leading to significant communication
overhead and long transmission time. To mitigate the communication bottleneck,
we resort to the hierarchical federated learning paradigm of HiFL, which reaps
the benefits of mobile edge computing and combines synchronous client-edge
model aggregation and asynchronous edge-cloud model aggregation together to
greatly reduce the traffic volumes of WAN transmissions. Specifically, we first
analyze the convergence bound of HiFL theoretically and identify the key
controllable factors for model performance improvement. We then advocate an
enhanced design of HiFlash by innovatively integrating deep reinforcement
learning based adaptive staleness control and heterogeneity-aware client-edge
association strategy to boost the system efficiency and mitigate the staleness
effect without compromising model accuracy. Extensive experiments corroborate
the superior performance of HiFlash in model accuracy, communication reduction,
and system efficiency.
