  Federated learning enables a cluster of decentralized mobile devices at the
edge to collaboratively train a shared machine learning model, while keeping
all the raw training samples on device. This decentralized training approach is
demonstrated as a practical solution to mitigate the risk of privacy leakage.
However, enabling efficient FL deployment at the edge is challenging because of
non-IID training data distribution, wide system heterogeneity and
stochastic-varying runtime effects in the field. This paper jointly optimizes
time-to-convergence and energy efficiency of state-of-the-art FL use cases by
taking into account the stochastic nature of edge execution. We propose AutoFL
by tailor-designing a reinforcement learning algorithm that learns and
determines which K participant devices and per-device execution targets for
each FL model aggregation round in the presence of stochastic runtime variance,
system and data heterogeneity. By considering the unique characteristics of FL
edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence
time and 4.7 and 5.2 times higher energy efficiency for local clients and
globally over the cluster of K participants, respectively.
