Quantum Federated Learning (QFL) enables distributed training of Quantum Machine Learning (QML) models by sharing model gradients instead of raw data. However, these gradients can still expose sensitive user information. To enhance privacy, homomorphic encryption of parameters has been proposed as a solution in QFL and related frameworks. In this work, we evaluate the overhead introduced by Fully Homomorphic Encryption (FHE) in QFL setups and assess its feasibility for real-world applications. We implemented various QML models including a Quantum Convolutional Neural Network (QCNN) trained in a federated environment with parameters encrypted using the CKKS scheme. This work marks the first QCNN trained in a federated setting with CKKS-encrypted parameters. Models of varying architectures were trained to predict brain tumors from MRI scans. The experiments reveal that memory and communication overhead remain substantial, making FHE challenging to deploy. Minimizing overhead requires reducing the number of model parameters, which, however, leads to a decline in classification performance, introducing a trade-off between privacy and model complexity.

翻译：量子联邦学习（QFL）通过共享模型梯度而非原始数据，实现了量子机器学习（QML）模型的分布式训练。然而，这些梯度仍可能泄露敏感的用户信息。为增强隐私性，在QFL及相关框架中，已提出对参数进行同态加密作为解决方案。本研究评估了全同态加密（FHE）在QFL设置中引入的开销，并评估了其在现实应用中的可行性。我们实现了多种QML模型，包括在联邦环境中训练、并使用CKKS方案加密参数的量子卷积神经网络（QCNN）。本工作标志着首个在联邦设置中使用CKKS加密参数训练的QCNN。我们训练了不同架构的模型，以从MRI扫描中预测脑肿瘤。实验结果表明，内存和通信开销仍然相当大，这使得FHE的部署面临挑战。最小化开销需要减少模型参数的数量，然而这会导致分类性能下降，从而在隐私性与模型复杂性之间引入了权衡。