  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.
