计算机科学
拜占庭式建筑
Byzantine容错
量子计算机
量子
上传
深度学习
理论计算机科学
人工智能
分布式计算
万维网
容错
古代史
物理
量子力学
历史
作者
Qi Xia,Zeyi Tao,Qun Li
标识
DOI:10.1109/msn53354.2021.00035
摘要
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.
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