Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
This work addresses privacy and reliability challenges in medical AI for clinical applications, though it appears incremental as it builds on existing federated learning and prompt-based techniques.
The authors tackled the problem of privacy-sensitive medical data in AI by developing a personalized federated learning method for medical visual question answering, which introduced learnable prompts and uncertainty quantification to enhance reliability and achieved efficient training without high computational costs.
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.