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arXiv · 2410.17484

Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering

He Zhu·Ren Togo·Takahiro Ogawa·Miki Haseyama
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Abstract

Conventional medical artificial intelligence (AI) models face barriers in clinical application and ethical issues owing to their inability to handle the privacy-sensitive characteristics of medical data. We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models, addressing privacy reliability challenges in the medical domain. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs. Then we introduce a reliable client VQA model that incorporates Dempster-Shafer evidence theory to quantify uncertainty in predictions, enhancing the model's reliability. Furthermore, we propose a novel inter-client communication mechanism that uses maximum likelihood estimation to balance accuracy and uncertainty, fostering efficient integration of insights across clients.

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He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama. 2024-10-23. Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering. https://arxiv.org/abs/2410.17484

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