  Private distributed learning studies the problem of how multiple distributed
entities collaboratively train a shared deep network with their private data
unrevealed. With the security provided by the protocols of blind quantum
computation, the cooperation between quantum physics and machine learning may
lead to unparalleled prospect for solving private distributed learning tasks.
In this paper, we introduce a quantum protocol for distributed learning that is
able to utilize the computational power of the remote quantum servers while
keeping the private data safe. For concreteness, we first introduce a protocol
for private single-party delegated training of variational quantum classifiers
based on blind quantum computing and then extend this protocol to multiparty
private distributed learning incorporated with differential privacy. We carry
out extensive numerical simulations with different real-life datasets and
encoding strategies to benchmark the effectiveness of our protocol. We find
that our protocol is robust to experimental imperfections and is secure under
the gradient attack after the incorporation of differential privacy. Our
results show the potential for handling computationally expensive distributed
learning tasks with privacy guarantees, thus providing a valuable guide for
exploring quantum advantages from the security perspective in the field of
machine learning with real-life applications.
