In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without
exchanging their data samples. This mechanism exploits the computational power
of all users and allows users to obtain a richer model as their models are
trained over a larger set of data points. However, this scheme only develops a
common output for all the users, and, therefore, it does not adapt the model to
each user. This is an important missing feature, especially given the
heterogeneity of the underlying data distribution for various users. In this
paper, we study a personalized variant of the federated learning in which our
goal is to find an initial shared model that current or new users can easily
adapt to their local dataset by performing one or a few steps of gradient
descent with respect to their own data. This approach keeps all the benefits of
the federated learning architecture, and, by structure, leads to a more
personalized model for each user. We show this problem can be studied within
the Model-Agnostic Meta-Learning (MAML) framework. Inspired by this connection,
we study a personalized variant of the well-known Federated Averaging algorithm
and evaluate its performance in terms of gradient norm for non-convex loss
functions. Further, we characterize how this performance is affected by the
closeness of underlying distributions of user data, measured in terms of
distribution distances such as Total Variation and 1-Wasserstein metric.

本文研究了联邦学习的个性化变种，在 Model-Agnostic Meta-Learning 框架下，通过个性化 Federated Averaging 算法，根据不同用户数据的分布差异，探讨其性能的影响。