  Federated Learning (FL) allows edge devices to collaboratively learn a shared
prediction model while keeping their training data on the device, thereby
decoupling the ability to do machine learning from the need to store data in
the cloud. Despite the algorithmic advancements in FL, the support for
on-device training of FL algorithms on edge devices remains poor. In this
paper, we present an exploration of on-device FL on various smartphones and
embedded devices using the Flower framework. We also evaluate the system costs
of on-device FL and discuss how this quantification could be used to design
more efficient FL algorithms.
