  Quantum Federated Learning (QFL) enables collaborative training of a Quantum
Machine Learning (QML) model among multiple clients possessing quantum
computing capabilities, without the need to share their respective local data.
However, the limited availability of quantum computing resources poses a
challenge for each client to acquire quantum computing capabilities. This
raises a natural question: Can quantum computing capabilities be deployed on
the server instead? In this paper, we propose a QFL framework specifically
designed for classical clients, referred to as CC-QFL, in response to this
question. In each iteration, the collaborative training of the QML model is
assisted by the shadow tomography technique, eliminating the need for quantum
computing capabilities of clients. Specifically, the server constructs a
classical representation of the QML model and transmits it to the clients. The
clients encode their local data onto observables and use this classical
representation to calculate local gradients. These local gradients are then
utilized to update the parameters of the QML model. We evaluate the
effectiveness of our framework through extensive numerical simulations using
handwritten digit images from the MNIST dataset. Our framework provides
valuable insights into QFL, particularly in scenarios where quantum computing
resources are scarce.
