Quantum federated learning through blind quantum computing

TL;DR

This paper proposes a quantum protocol for private distributed learning that leverages blind quantum computing to ensure data privacy while utilizing remote quantum servers, demonstrating robustness and security in simulations.

Contribution

It introduces a novel quantum protocol for private distributed learning using blind quantum computing, extending to multiparty settings with differential privacy.

Findings

Protocol is robust to experimental imperfections.

Secure against gradient attacks with differential privacy.

Effective on real-life datasets with various encoding strategies.

Abstract

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.