  Federated learning (FL) is getting increased attention for processing
sensitive, distributed datasets common to domains such as healthcare. Instead
of directly training classification models on these datasets, recent works have
considered training data generators capable of synthesising a new dataset which
is not protected by any privacy restrictions. Thus, the synthetic data can be
made available to anyone, which enables further evaluation of machine learning
architectures and research questions off-site. As an additional layer of
privacy-preservation, differential privacy can be introduced into the training
process. We propose DPD-fVAE, a federated Variational Autoencoder with
Differentially-Private Decoder, to synthesise a new, labelled dataset for
subsequent machine learning tasks. By synchronising only the decoder component
with FL, we can reduce the privacy cost per epoch and thus enable better data
generators. In our evaluation on MNIST, Fashion-MNIST and CelebA, we show the
benefits of DPD-fVAE and report competitive performance to related work in
terms of Fr\'echet Inception Distance and accuracy of classifiers trained on
the synthesised dataset.
