Defending Against Byzantine Attacks in Quantum Federated Learning

By combining the advantages of both quantum computing and deep learning, quantum neural networks have become popular in recent research. In order to collaborate multiple quantum machines with local training data to train a global model, quantum federated learning is proposed. However, similar to classic federated learning, when communicating with multiple machines, quantum federated learning also faces the threats of Byzantine attacks. The byzantine attack is a kind of attack in a distributed system when some machines upload malicious information instead of the honest computational results to the server. In this article, we compare the differences of Byzantine problems between classic distributed learning and quantum federated learning, and modify the previously proposed four kinds of Byzantine tolerant algorithms to the quantum version. We conduct simulated experiments to show a similar performance of the quantum version with the classic version.