标题： 基于嵌入的联邦数据共享通过差分隐私条件变分自编码器 显示英文标题

标题： Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs

摘要： 深度学习（DL）已经革新了医学影像，但其采用受到数据稀缺性和隐私法规的限制，限制了对多样化数据集的访问。 联邦学习（FL）实现了去中心化训练，但存在高通信成本，并且通常仅限于单一下游任务，降低了灵活性。 我们提出了一种通过差分隐私（DP）生成模型进行数据共享的方法。 通过采用基础模型，我们提取出紧凑且信息丰富的嵌入，减少冗余并降低计算开销。 客户端协作训练一个差分隐私条件变分自编码器（DP-CVAE），以建模全局的、具有隐私意识的数据分布，支持多种下游任务。 我们的方法在多个特征提取器上进行了验证，增强了隐私性、可扩展性和效率，在确保差分隐私的同时，优于传统的FL分类器。 此外，DP-CVAE生成的嵌入质量高于DP-CGAN，同时需要$5{\times}$更少的参数。 显示英文摘要

摘要： Deep Learning (DL) has revolutionized medical imaging, yet its adoption is constrained by data scarcity and privacy regulations, limiting access to diverse datasets. Federated Learning (FL) enables decentralized training but suffers from high communication costs and is often restricted to a single downstream task, reducing flexibility. We propose a data-sharing method via Differentially Private (DP) generative models. By adopting foundation models, we extract compact, informative embeddings, reducing redundancy and lowering computational overhead. Clients collaboratively train a Differentially Private Conditional Variational Autoencoder (DP-CVAE) to model a global, privacy-aware data distribution, supporting diverse downstream tasks. Our approach, validated across multiple feature extractors, enhances privacy, scalability, and efficiency, outperforming traditional FL classifiers while ensuring differential privacy. Additionally, DP-CVAE produces higher-fidelity embeddings than DP-CGAN while requiring $5{\times}$ fewer parameters.