  Over the last years, topic modeling has emerged as a powerful technique for
organizing and summarizing big collections of documents or searching for
particular patterns in them. However, privacy concerns may arise when
cross-analyzing data from different sources. Federated topic modeling solves
this issue by allowing multiple parties to jointly train a topic model without
sharing their data. While several federated approximations of classical topic
models do exist, no research has been conducted on their application for neural
topic models. To fill this gap, we propose and analyze a federated
implementation based on state-of-the-art neural topic modeling implementations,
showing its benefits when there is a diversity of topics across the nodes'
documents and the need to build a joint model. In practice, our approach is
equivalent to a centralized model training, but preserves the privacy of the
nodes. Advantages of this federated scenario are illustrated by means of
experiments using both synthetic and real data scenarios.
