  Federated Learning (FL) refers to learning a high quality global model based
on decentralized data storage, without ever copying the raw data. A natural
scenario arises with data created on mobile phones by the activity of their
users. Given the typical data heterogeneity in such situations, it is natural
to ask how can the global model be personalized for every such device,
individually. In this work, we point out that the setting of Model Agnostic
Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot
adaptation to a heterogeneous distribution of tasks, has a number of
similarities with the objective of personalization for FL. We present FL as a
natural source of practical applications for MAML algorithms, and make the
following observations. 1) The popular FL algorithm, Federated Averaging, can
be interpreted as a meta learning algorithm. 2) Careful fine-tuning can yield a
global model with higher accuracy, which is at the same time easier to
personalize. However, solely optimizing for the global model accuracy yields a
weaker personalization result. 3) A model trained using a standard datacenter
optimization method is much harder to personalize, compared to one trained
using Federated Averaging, supporting the first claim. These results raise new
questions for FL, MAML, and broader ML research.
