  Federated learning methods enable us to train machine learning models on
distributed user data while preserving its privacy. However, it is not always
feasible to obtain high-quality supervisory signals from users, especially for
vision tasks. Unlike typical federated settings with labeled client data, we
consider a more practical scenario where the distributed client data is
unlabeled, and a centralized labeled dataset is available on the server. We
further take the server-client and inter-client domain shifts into account and
pose a domain adaptation problem with one source (centralized server data) and
multiple targets (distributed client data). Within this new Federated
Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance
of exiting domain adaptation methods and propose an effective DualAdapt method
to address the new challenges. Extensive experimental results on image
classification and semantic segmentation tasks demonstrate that our method
achieves high accuracy, incurs minimal communication cost, and requires low
computational resources on client devices.
