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.

翻译：联邦学习（FL）是一种允许多个参与者协作训练深度神经网络（DNN）而无需集中存储各自数据的技术。除其他优势外，其固有的隐私保护特性使其在医疗或军事等敏感场景中具有应用吸引力。尽管数据未直接交换，但训练过程需要共享参与者模型信息，这使得个体模型易遭恶意行为者窃取或未经授权分发。为应对机器学习（ML）中的所有权保护问题，近五年来深度神经网络水印方法应运而生。现有研究主要聚焦集中式水印技术，但仅有少数方法针对联邦学习及其独特约束条件而设计。本文综述联邦学习水印技术的最新进展，阐明该领域涌现的新挑战与机遇。