用隐私保护生成模型实现高效医疗数据共享，支持多任务应用。
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs
- 通过私密条件变分自编码器生成高保真嵌入，降低通信开销。
- 相比传统联邦学习，模型参数减少5倍，且在多任务上表现更优。
- 适合需要隐私保护与跨任务扩展的医疗影像研究者使用。
深度学习虽革新了医学影像分析，但受限于数据稀缺和隐私法规，难以获取多样化数据集。联邦学习虽可实现去中心化训练，却面临通信成本高、仅支持单一下游任务的问题。本文提出基于差分隐私的生成式数据共享方法：利用基础模型提取紧凑、信息丰富的嵌入，减少冗余并降低计算负担；客户端协作训练差分隐私条件变分自编码器（DP-CVAE），建模全局隐私感知的数据分布，支持多种下游任务。实验验证该方法在多个特征提取器下均提升隐私性、可扩展性与效率，优于传统联邦分类器，且确保差分隐私。此外，DP-CVAE生成的嵌入质量高于DP-CGAN，同时参数量仅为后者的1/5。
原文摘要 · Abstract (English)
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.
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