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Federated learning (FL), training deep models from decentralized data withoutprivacy leakage, has shown great potential in medical image computing recently.However, considering the ubiquitous class imbalance in medical data, FL canexhibit performance degradation, especially for minority classes (e.g. rarediseases). Existing methods towards this problem mainly focus on training abalanced classifier to eliminate class prior bias among classes, but neglect toexplore better representation to facilitate classification performance. In thispaper, we present a privacy-preserving FL method named FedIIC to combat classimbalance from two perspectives: feature learning and classifier learning. Infeature learning, two levels of contrastive learning are designed to extractbetter class-specific features with imbalanced data in FL. In classifierlearning, per-class margins are dynamically set according to real-timedifficulty and class priors, which helps the model learn classes equally.Experimental results on publicly-available datasets demonstrate the superiorperformance of FedIIC in dealing with both real-world and simulatedmulti-source medical imaging data under class imbalance. Code is available athttps://github.com/wnn2000/FedIIC.

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