  The preservation of privacy is a critical concern in the implementation of
artificial intelligence on sensitive training data. There are several
techniques to preserve data privacy but quantum computations are inherently
more secure due to the no-cloning theorem, resulting in a most desirable
computational platform on top of the potential quantum advantages. There have
been prior works in protecting data privacy by Quantum Federated Learning (QFL)
and Quantum Differential Privacy (QDP) studied independently. However, to the
best of our knowledge, no prior work has addressed both QFL and QDP together
yet. Here, we propose to combine these privacy-preserving methods and implement
them on the quantum platform, so that we can achieve comprehensive protection
against data leakage (QFL) and model inversion attacks (QDP). This
implementation promises more efficient and secure artificial intelligence. In
this paper, we present a successful implementation of these
privacy-preservation methods by performing the binary classification of the
Cats vs Dogs dataset. Using our quantum-classical machine learning model, we
obtained a test accuracy of over 0.98, while maintaining epsilon values less
than 1.3. We show that federated differentially private training is a viable
privacy preservation method for quantum machine learning on Noisy
Intermediate-Scale Quantum (NISQ) devices.
