Federated learning allows clients to collaboratively learn statistical models
while keeping their data local. Federated learning was originally used to train
a unique global model to be served to all clients, but this approach might be
sub-optimal when clients' local data distributions are heterogeneous. In order
to tackle this limitation, recent personalized federated learning methods train
a separate model for each client while still leveraging the knowledge available
at other clients. In this work, we exploit the ability of deep neural networks
to extract high quality vectorial representations (embeddings) from non-tabular
data, e.g., images and text, to propose a personalization mechanism based on
local memorization. Personalization is obtained by interpolating a collectively
trained global model with a local k-nearest neighbors (kNN) model based on
the shared representation provided by the global model. We provide
generalization bounds for the proposed approach in the case of binary
classification, and we show on a suite of federated datasets that this approach
achieves significantly higher accuracy and fairness than state-of-the-art
methods.Comment: 23 pages, ICML 202