Privacy-preserving quantum federated learning via gradient hiding
Changhao Li Niraj Kumar Zhixin Song Shouvanik Chakrabarti Marco Pistoia
Distributed quantum computing, particularly distributed quantum machine
learning, has gained substantial prominence for its capacity to harness the
collective power of distributed quantum resources, transcending the limitations
of individual quantum nodes. Meanwhile, the critical concern of privacy within
distributed computing protocols remains a significant challenge, particularly
in standard classical federated learning (FL) scenarios where data of
participating clients is susceptible to leakage via gradient inversion attacks
by the server. This paper presents innovative quantum protocols with quantum
communication designed to address the FL problem, strengthen privacy measures,
and optimize communication efficiency. In contrast to previous works that
leverage expressive variational quantum circuits or differential privacy
techniques, we consider gradient information concealment using quantum states
and propose two distinct FL protocols, one based on private inner-product
estimation and the other on incremental learning. These protocols offer
substantial advancements in privacy preservation with low communication
resources, forging a path toward efficient quantum communication-assisted FL
protocols and contributing to the development of secure distributed quantum
machine learning, thus addressing critical privacy concerns in the quantum
computing era.