  Federated Learning (FL) is a technique that allows multiple participants to
collaboratively train a Deep Neural Network (DNN) without the need of
centralizing their data. Among other advantages, it comes with
privacy-preserving properties making it attractive for application in sensitive
contexts, such as health care or the military. Although the data are not
explicitly exchanged, the training procedure requires sharing information about
participants' models. This makes the individual models vulnerable to theft or
unauthorized distribution by malicious actors. To address the issue of
ownership rights protection in the context of Machine Learning (ML), DNN
Watermarking methods have been developed during the last five years. Most
existing works have focused on watermarking in a centralized manner, but only a
few methods have been designed for FL and its unique constraints. In this
paper, we provide an overview of recent advancements in Federated Learning
watermarking, shedding light on the new challenges and opportunities that arise
in this field.
