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%).

本文设计了一种称为 HybridFL 的多层联邦学习协议，该协议结合了边缘节点和云端两层不同的模型聚合策略，并使用概率方法在不确定的末端设备状态下通过区域弹性因子来选择客户端，实验证明了该协议在不同规模的 MEC 系统中显著提高了 FL 训练过程的效率，缩短了联邦轮次长度，加速了全局模型的收敛速度（最多快 12 倍），并降低了末端设备的能耗（最多降低 58%）。