  Being very low power, the use of neuromorphic processors in mobile devices to
solve machine learning problems is a promising alternative to traditional Von
Neumann processors. Federated Learning enables entities such as mobile devices
to collaboratively learn a shared model while keeping their training data
local. Additionally, federated learning is a secure way of learning because
only the model weights need to be shared between models, keeping the data
private. Here we demonstrate the efficacy of federated learning in neuromorphic
processors. Neuromorphic processors benefit from the collaborative learning,
achieving state of the art accuracy on a one-shot learning gesture recognition
task across individual processor models while preserving local data privacy.
