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.

翻译：我们提出了一种新颖的基于提示的个性化联邦学习（pFL）方法，以解决传统医学视觉问答（VQA）方法中的数据异构性和隐私问题。具体而言，我们将来自不同器官的医学数据集视为客户端，并使用pFL为每个客户端训练基于Transformer的个性化VQA模型。针对先前pFL方法中客户端间通信计算复杂度高的问题，我们通过引入作为小型可学习参数的提示，构建了一个简洁的信息共享系统。此外，所提方法引入了一个可靠性参数，以避免低性能和无关客户端带来的负面影响。最后，在多种异构医学数据集上的广泛评估证明了我们方法的有效性。