Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs
This work addresses privacy and efficiency challenges in federated learning for medical imaging, offering an incremental improvement over existing methods.
The paper tackles the problem of data scarcity and privacy in medical imaging by proposing a federated data-sharing method using differentially private conditional VAEs, which reduces communication costs by 5x fewer parameters and outperforms traditional FL classifiers while ensuring privacy.
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.