  Millions of patients suffer from rare diseases around the world. However, the
samples of rare diseases are much smaller than those of common diseases. In
addition, due to the sensitivity of medical data, hospitals are usually
reluctant to share patient information for data fusion citing privacy concerns.
These challenges make it difficult for traditional AI models to extract rare
disease features for the purpose of disease prediction. In this paper, we
overcome this limitation by proposing a novel approach for rare disease
prediction based on federated meta-learning. To improve the prediction accuracy
of rare diseases, we design an attention-based meta-learning (ATML) approach
which dynamically adjusts the attention to different tasks according to the
measured training effect of base learners. Additionally, a dynamic-weight based
fusion strategy is proposed to further improve the accuracy of federated
learning, which dynamically selects clients based on the accuracy of each local
model. Experiments show that with as few as five shots, our approach
out-performs the original federated meta-learning algorithm in accuracy and
speed. Compared with each hospital's local model, the proposed model's average
prediction accuracy increased by 13.28%.
