Deep Generative Models Meet Federated Learning: A Healthcare-Centered Review

The integration of Artificial Intelligence (AI) in healthcare holds significant promise, yet remains constrained by data scarcity, privacy and ethical concerns, and the inherent complexity of medical tasks. This review explores the intersection of deep generative modeling and federated learning, two complementary paradigms that enable collaborative and privacy-preserving innovation. We survey recent trends in deep generative models applied to healthcare, with a particular focus on federated settings. The review is structured around model architectures, providing a taxonomy-driven analysis that emphasizes solutions tailored to the heterogeneity of healthcare data. Although this review initially intended to cover multimodal applications, the current literature is largely limited to single-modality models. Among deep generative approaches, Generative Adversarial Networks dominate the field, particularly in domains such as medical image synthesis and data augmentation. In contrast, alternative architectures, including variational autoencoders, diffusion models, and autoregressive models, remain barely explored in federated healthcare scenarios. Notably, the number of related publications has increased substantially in recent years, rising from 1 in 2020 to 16 in 2024, reflecting the growing interest and momentum in this area. This paper highlights key challenges, open research questions, and future directions for the development of trustworthy, distributed generative AI in healthcare.

Recommended citation: Ceresi, A., Galende, B. A., Guinea-Pérez, J., Apellániz, P. A., Hernández-Peñaloza, G., & Álvarez, F. (2025). Deep Generative Models Meet Federated Learning: A Healthcare-Centered Review. Authorea Preprints. /files/2025-08-26-dgm-fl-review.pdf