FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification

TL;DR

FedIIC introduces a privacy-preserving federated learning approach that enhances class-specific feature extraction and dynamically adjusts class margins to improve medical image classification under severe class imbalance.

Contribution

The paper proposes FedIIC, a novel federated learning method combining contrastive feature learning and dynamic class margins to address class imbalance in medical imaging.

Findings

FedIIC outperforms existing methods on public datasets.

Effective in real-world and simulated multi-source data.

Improves minority class recognition in imbalanced datasets.

Abstract

Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://github.com/wnn2000/FedIIC.