  Federated edge learning (FEEL) is a widely adopted framework for training an
artificial intelligence (AI) model distributively at edge devices to leverage
their data while preserving their data privacy. The execution of a power-hungry
learning task at energy-constrained devices is a key challenge confronting the
implementation of FEEL. To tackle the challenge, we propose the solution of
powering devices using wireless power transfer (WPT). To derive guidelines on
deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work
aims at the derivation of the tradeoff between the model convergence and the
settings of power sources in two scenarios: 1) the transmission power and
density of power-beacons (dedicated charging stations) if they are deployed, or
otherwise 2) the transmission power of a server (access-point). The development
of the proposed analytical framework relates the accuracy of distributed
stochastic gradient estimation to the WPT settings, the randomness in both
communication and WPT links, and devices' computation capacities. Furthermore,
the local-computation at devices (i.e., mini-batch size and processor clock
frequency) is optimized to efficiently use the harvested energy for gradient
estimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws
of the model-convergence rate with respect to the transferred energy as well as
the devices' computational energy efficiencies. The results provide useful
guidelines on WPT provisioning to provide a guaranteer on learning performance.
They are corroborated by experimental results using a real dataset.
