文献类型：期刊文献

英文题名：An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis

作者：Wang, Ming[1]; Duan, Zhaoyang[1]; Xue, Dong[1]; Liu, Fangzhou[2]; Zhang, Zhongheng[3,4]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Research Institute of Intelligent Control and Systems, School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China; [3] Department of Emergency Medicine, Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China; [4] School of Medicine, Shaoxing University, Shaoxing, 312000, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250331675)

语种：英文

外文关键词：Data Sharing - Differential privacy - Federated learning - Gradient methods - Labeled data - Medical computing - Medical image processing - Neural networks - Privacy-preserving techniques - Pulmonary diseases

摘要：The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions. Copyright ? 2025, The Authors. All rights reserved.