  Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end
devices, has shown great potential in bringing data processing closer to the
data sources. Meanwhile, Federated learning (FL) has emerged as a promising
privacy-preserving approach to facilitating AI applications. However, it
remains a big challenge to optimize the efficiency and effectiveness of FL when
it is integrated with the MEC architecture. Moreover, the unreliable nature
(e.g., stragglers and intermittent drop-out) of end devices significantly slows
down the FL process and affects the global model's quality Xin such
circumstances. In this paper, a multi-layer federated learning protocol called
HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the
edge level and the cloud level) of model aggregation enacting different
aggregation strategies. Moreover, in order to mitigate stragglers and end
device drop-out, we introduce regional slack factors into the stage of client
selection performed at the edge nodes using a probabilistic approach without
identifying or probing the state of end devices (whose reliability is
agnostic). We demonstrate the effectiveness of our method in modulating the
proportion of clients selected and present the convergence analysis for our
protocol. We have conducted extensive experiments with machine learning tasks
in different scales of MEC system. The results show that HybridFL improves the
FL training process significantly in terms of shortening the federated round
length, speeding up the global model's convergence (by up to 12X) and reducing
end device energy consumption (by up to 58%).
