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Despite recent progress on semi-supervised federated learning (FL) formedical image diagnosis, the problem of imbalanced class distributions amongunlabeled clients is still unsolved for real-world use. In this paper, we studya practical yet challenging problem of class imbalanced semi-supervised FL(imFed-Semi), which allows all clients to have only unlabeled data while theserver just has a small amount of labeled data. This imFed-Semi problem isaddressed by a novel dynamic bank learning scheme, which improves clienttraining by exploiting class proportion information. This scheme consists oftwo parts, i.e., the dynamic bank construction to distill various classproportions for each local client, and the sub-bank classification to imposethe local model to learn different class proportions. We evaluate our approachon two public real-world medical datasets, including the intracranialhemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with10,015 dermoscopy images. The effectiveness of our method has been validatedwith significant performance improvements (7.61% and 4.69%) compared with thesecond-best on the accuracy, as well as comprehensive analytical studies. Codeis available at https://github.com/med-air/imFedSemi.

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance