  Federated Learning (FL) can be used in mobile edge networks to train machine
learning models in a distributed manner. Recently, FL has been interpreted
within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL
significant advantages in fast adaptation and convergence over heterogeneous
datasets. However, existing research simply combines MAML and FL without
explicitly addressing how much benefit MAML brings to FL and how to maximize
such benefit over mobile edge networks. In this paper, we quantify the benefit
from two aspects: optimizing FL hyperparameters (i.e., sampled data size and
the number of communication rounds) and resource allocation (i.e., transmit
power) in mobile edge networks. Specifically, we formulate the MAML-based FL
design as an overall learning time minimization problem, under the constraints
of model accuracy and energy consumption. Facilitated by the convergence
analysis of MAML-based FL, we decompose the formulated problem and then solve
it using analytical solutions and the coordinate descent method. With the
obtained FL hyperparameters and resource allocation, we design a MAML-based FL
algorithm, called Automated Federated Learning (AutoFL), that is able to
conduct fast adaptation and convergence. Extensive experimental results verify
that AutoFL outperforms other benchmark algorithms regarding the learning time
and convergence performance.
