# Prompt-based Personalized Federated Learning for Medical Visual Question Answering Source: [Prompt-based Personalized Federated Learning for Medical Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:30:07.706Z Authors: He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama Published: 2024-02-15T03:09:54.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()