Quantum Federated Learning for Distributed Quantum Networks

Abstract

Federated learning is a framework that can learn from distributed networks. It attempts to build a global model based on virtual fusion data without sharing the actual data. Nevertheless, the traditional federated learning process encounters two main challenges: high computational cost and message transmission security. To address these challenges, we propose a quantum federated learning for distributed quantum networks by utilizing interesting characteristics of quantum mechanics. First, we give two methods to extract the data information to the quantum state. It can cope with different acquisition frequencies of data information. Next, a quantum gradient descent algorithm is provided to help clients in the distributed quantum networks to train local models. In other words, the algorithm gives the clients a mechanism to estimate the gradient of the local model in parallel. Compared with the classical counterpart, the proposed algorithm achieves exponential acceleration in dataset scale and quadratic speedup in data dimensionality. And, a quantum secure multi-party computation protocol is designed, which utilizes the Chinese residual theorem. It could avoid errors and overflow problems that may occur in the process of large number operation. Security analysis shows that this quantum protocol can resist common external and internal attacks. Finally, to demonstrate the effectiveness of the proposed framework, we use it to the train federated linear regression model and execute essential computation steps on the Qiskit quantum computing framework.