Abstract
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.
Abstract (translated)
医疗数据标注的劳动密集型特性对呼吸道疾病诊断构成了重大挑战，导致资源受限环境中高质量标记数据集的稀缺。此外，患者隐私问题使得难以直接跨机构共享本地医学数据，并且现有的集中式数据驱动方法依赖于大量可用的数据，在保护数据隐私方面往往存在妥协。本研究提出了一种带有隐私保护机制的联邦小样本学习框架，以解决在诊断呼吸道疾病时面临的有限标注数据和隐私保护的问题。具体来说，提出了一个元随机梯度下降算法来减轻由于缺乏数据而在使用传统梯度下降方法训练神经网络时出现的过拟合问题。此外，为了确保在本地数据上训练私有模型时对抗梯度泄露的数据隐私，在梯度中加入了来自标准高斯分布的差分隐私噪声，从而防止医学图像的重建。鉴于集中化分散于不同医疗机构中的呼吸道疾病数据不切实际，采用了加权平均算法来聚合来自不同客户端的局部诊断模型，以增强模型在各种场景下的适应性。实验结果表明，所提出的方法在实现差分隐私的情况下取得了令人信服的结果，并且能够有效地使用不同结构、类别和分布的数据进行呼吸道疾病的诊断。
URL
https://arxiv.org/abs/2507.08050
