  We present a novel prompt-based personalized federated learning (pFL) method
to address data heterogeneity and privacy concerns in traditional medical
visual question answering (VQA) methods. Specifically, we regard medical
datasets from different organs as clients and use pFL to train personalized
transformer-based VQA models for each client. To address the high computational
complexity of client-to-client communication in previous pFL methods, we
propose a succinct information sharing system by introducing prompts that are
small learnable parameters. In addition, the proposed method introduces a
reliability parameter to prevent the negative effects of low performance and
irrelevant clients. Finally, extensive evaluations on various heterogeneous
medical datasets attest to the effectiveness of our proposed method.
