Federated Learning (FL) has emerged as a solution for distributed systems that allow clients to train models on their data and only share models instead of local data. Generative Models are designed to learn the distribution of a dataset and generate new data samples that are similar to the original data. Many prior works have tried proposing Federated Generative Models. Using Federated Learning and Generative Models together can be susceptible to attacks, and designing the optimal architecture remains challenging. This survey covers the growing interest in the intersection of FL and Generative Models by comprehensively reviewing research conducted from 2019 to 2024. We systematically compare nearly 100 papers, focusing on their FL and Generative Model methods and privacy considerations. To make this field more accessible to newcomers, we highlight the state-of-the-art advancements and identify unresolved challenges, offering insights for future research in this evolving field.

翻译：联邦学习（FL）已成为分布式系统的一种解决方案，它允许客户端在其本地数据上训练模型，并仅共享模型而非原始数据。生成模型旨在学习数据集的分布，并生成与原始数据相似的新数据样本。先前已有许多研究尝试提出联邦生成模型。将联邦学习与生成模型结合使用可能易受攻击，且设计最优架构仍具挑战性。本综述通过全面回顾2019年至2024年的研究，涵盖了联邦学习与生成模型交叉领域日益增长的研究兴趣。我们系统性地比较了近100篇论文，重点关注其联邦学习与生成模型方法以及隐私考量。为使该领域更易于新研究者入门，我们重点介绍了最先进的进展，并指出了尚未解决的挑战，为这一不断发展的领域的未来研究提供了见解。