Healthcare industries frequently handle sensitive and proprietary data, and
due to strict privacy regulations, they are often reluctant to share data
directly. In today's context, Federated Learning (FL) stands out as a crucial
remedy, facilitating the rapid advancement of distributed machine learning
while effectively managing critical concerns regarding data privacy and
governance. The fusion of federated learning and quantum computing represents a
groundbreaking interdisciplinary approach with immense potential to
revolutionize various industries, from healthcare to finance. In this work, we
proposed a federated learning framework based on quantum tensor networks, which
leverages the principles of many-body quantum physics. Currently, there are no
known classical tensor networks implemented in federated settings. Furthermore,
we investigated the effectiveness and feasibility of the proposed framework by
conducting a differential privacy analysis to ensure the security of sensitive
data across healthcare institutions. Experiments on popular medical image
datasets show that the federated quantum tensor network model achieved a mean
receiver-operator characteristic area under the curve (ROC-AUC) between
0.91-0.98. Experimental results demonstrate that the quantum federated global
model, consisting of highly entangled tensor network structures, showed better
generalization and robustness and achieved higher testing accuracy, surpassing
the performance of locally trained clients under unbalanced data distributions
among healthcare institutions.

提出了基于量子张量网络的联邦学习框架，通过多体量子物理原理充分利用，进行了不平衡数据分布下的差分隐私分析，实验结果显示，联邦量子张量网络模型在医疗图像数据集上达到了 0.91-0.98 的平均接收者操作特征曲线下面积（ROC-AUC），在医疗院所之间的不平衡数据分布下，量子联邦全局模型表现出更好的泛化性能和鲁棒性，实现了更高的测试准确率，超过了本地训练客户端的表现。