  Federated Learning has gained popularity among medical institutions since it
enables collaborative training between clients (e.g., hospitals) without
aggregating data. However, due to the high cost associated with creating
annotations, especially for large 3D image datasets, clinical institutions do
not have enough supervised data for training locally. Thus, the performance of
the collaborative model is subpar under limited supervision. On the other hand,
large institutions have the resources to compile data repositories with
high-resolution images and labels. Therefore, individual clients can utilize
the knowledge acquired in the public data repositories to mitigate the shortage
of private annotated images. In this paper, we propose a federated few-shot
learning method with dual knowledge distillation. This method allows joint
training with limited annotations across clients without jeopardizing privacy.
The supervised learning of the proposed method extracts features from limited
labeled data in each client, while the unsupervised data is used to distill
both feature and response-based knowledge from a national data repository to
further improve the accuracy of the collaborative model and reduce the
communication cost. Extensive evaluations are conducted on 3D magnetic
resonance knee images from a private clinical dataset. Our proposed method
shows superior performance and less training time than other semi-supervised
federated learning methods. Codes and additional visualization results are
available at https://github.com/hexiaoxiao-cs/fedml-knee.
