  Federated learning improves data privacy and efficiency in machine learning
performed over networks of distributed devices, such as mobile phones, IoT and
wearable devices, etc. Yet models trained with federated learning can still
fail to generalize to new devices due to the problem of domain shift. Domain
shift occurs when the labeled data collected by source nodes statistically
differs from the target node's unlabeled data. In this work, we present a
principled approach to the problem of federated domain adaptation, which aims
to align the representations learned among the different nodes with the data
distribution of the target node. Our approach extends adversarial adaptation
techniques to the constraints of the federated setting. In addition, we devise
a dynamic attention mechanism and leverage feature disentanglement to enhance
knowledge transfer. Empirically, we perform extensive experiments on several
image and text classification tasks and show promising results under
unsupervised federated domain adaptation setting.
