  Federated Learning (FL) is a decentralized learning paradigm, in which
multiple clients collaboratively train deep learning models without
centralizing their local data, and hence preserve data privacy. Real-world
applications usually involve a distribution shift across the datasets of the
different clients, which hurts the generalization ability of the clients to
unseen samples from their respective data distributions. In this work, we
address the recently proposed feature shift problem where the clients have
different feature distributions, while the label distribution is the same. We
propose Federated Representation Augmentation (FRAug) to tackle this practical
and challenging problem. Our approach generates synthetic client-specific
samples in the embedding space to augment the usually small client datasets.
For that, we train a shared generative model to fuse the clients knowledge
learned from their different feature distributions. This generator synthesizes
client-agnostic embeddings, which are then locally transformed into
client-specific embeddings by Representation Transformation Networks (RTNets).
By transferring knowledge across the clients, the generated embeddings act as a
regularizer for the client models and reduce overfitting to the local original
datasets, hence improving generalization. Our empirical evaluation on public
benchmarks and a real-world medical dataset demonstrates the effectiveness of
the proposed method, which substantially outperforms the current
state-of-the-art FL methods for non-IID features, including PartialFed and
FedBN.
