  Distributed training across several quantum computers could significantly
improve the training time and if we could share the learned model, not the
data, it could potentially improve the data privacy as the training would
happen where the data is located. However, to the best of our knowledge, no
work has been done in quantum machine learning (QML) in federation setting yet.
In this work, we present the federated training on hybrid quantum-classical
machine learning models although our framework could be generalized to pure
quantum machine learning model. Specifically, we consider the quantum neural
network (QNN) coupled with classical pre-trained convolutional model. Our
distributed federated learning scheme demonstrated almost the same level of
trained model accuracies and yet significantly faster distributed training. It
demonstrates a promising future research direction for scaling and privacy
aspects.
