  We describe the design of our federated task processing system. Originally,
the system was created to support two specific federated tasks: evaluation and
tuning of on-device ML systems, primarily for the purpose of personalizing
these systems. In recent years, support for an additional federated task has
been added: federated learning (FL) of deep neural networks. To our knowledge,
only one other system has been described in literature that supports FL at
scale. We include comparisons to that system to help discuss design decisions
and attached trade-offs. Finally, we describe two specific large scale
personalization use cases in detail to showcase the applicability of federated
tuning to on-device personalization and to highlight application specific
solutions.
