  Federated learning (FL) is a heavily promoted approach for training ML models
on sensitive data, e.g., text typed by users on their smartphones. FL is
expressly designed for training on data that are unbalanced and non-iid across
the participants. To ensure privacy and integrity of the fedeated model, latest
FL approaches use differential privacy or robust aggregation.
  We look at FL from the \emph{local} viewpoint of an individual participant
and ask: (1) do participants have an incentive to participate in FL? (2) how
can participants \emph{individually} improve the quality of their local models,
without re-designing the FL framework and/or involving other participants?
  First, we show that on standard tasks such as next-word prediction, many
participants gain no benefit from FL because the federated model is less
accurate on their data than the models they can train locally on their own.
Second, we show that differential privacy and robust aggregation make this
problem worse by further destroying the accuracy of the federated model for
many participants.
  Then, we evaluate three techniques for local adaptation of federated models:
fine-tuning, multi-task learning, and knowledge distillation. We analyze where
each is applicable and demonstrate that all participants benefit from local
adaptation. Participants whose local models are poor obtain big accuracy
improvements over conventional FL. Participants whose local models are better
than the federated model\textemdash and who have no incentive to participate in
FL today\textemdash improve less, but sufficiently to make the adapted
federated model better than their local models.
