  With the fast development of quantum computing and deep learning, quantum
neural networks have attracted great attention recently. By leveraging the
power of quantum computing, deep neural networks can potentially overcome
computational power limitations in classic machine learning. However, when
multiple quantum machines wish to train a global model using the local data on
each machine, it may be very difficult to copy the data into one machine and
train the model. Therefore, a collaborative quantum neural network framework is
necessary. In this article, we borrow the core idea of federated learning to
propose QuantumFed, a quantum federated learning framework to have multiple
quantum nodes with local quantum data train a mode together. Our experiments
show the feasibility and robustness of our framework.
