Prompt-based Personalized Federated Learning for Medical Visual Question Answering
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningMedical Visual Question AnsweringPersonalized Federated LearningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Similar Papers 제목 키워드 기반
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
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 nov…
Federated LearningMedical Visual Question AnsweringPersonalized Federated LearningQuestion Answering+2Visual Prompt Based Personalized Federated Learning
As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowledge from all distributed clients. Most e…
Federated Learningimage-classificationImage ClassificationPersonalized Federated LearningEfficient Model Personalization in Federated Learning via Client-Specific Prompt Generation
Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently, large-scale pre-trained models (e.g., …
Federated LearningFedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models gr…
DecoderFederated LearningImage SegmentationMedical Image Segmentation+3Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw data. Personalized federated learning (…
Personalized Federated LearningVisual Prompt Tuning