FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation

TL;DR

FRAug introduces a novel federated learning approach that generates synthetic embeddings to address non-IID feature distributions across clients, significantly improving model generalization and outperforming existing methods.

Contribution

The paper proposes Federated Representation Augmentation (FRAug), a new method that synthesizes client-specific embeddings to mitigate feature shift in federated learning.

Findings

Outperforms state-of-the-art FL methods like PartialFed and FedBN

Effective on public benchmarks and real-world medical data

Reduces overfitting and improves generalization

Abstract

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.