The privacy in classical federated learning can be breached through the use of local gradient results along with engineered queries to the clients. However, quantum communication channels are considered more secure because a measurement on the channel causes a loss of information, which can be detected by the sender. Therefore, the quantum version of federated learning can be used to provide more privacy. Additionally, sending an $N$ dimensional data vector through a quantum channel requires sending $\log N$ entangled qubits, which can potentially provide exponential efficiency if the data vector is utilized as quantum states. In this paper, we propose a quantum federated learning model where fixed design quantum chips are operated based on the quantum states sent by a centralized server. Based on the coming superposition states, the clients compute and then send their local gradients as quantum states to the server, where they are aggregated to update parameters. Since the server does not send model parameters, but instead sends the operator as a quantum state, the clients are not required to share the model. This allows for the creation of asynchronous learning models. In addition, the model as a quantum state is fed into client-side chips directly; therefore, it does not require measurements on the upcoming quantum state to obtain model parameters in order to compute gradients. This can provide efficiency over the models where the parameter vector is sent via classical or quantum channels and local gradients are obtained through the obtained values of these parameters.

翻译：经典联邦学习中的隐私可能通过使用本地梯度结果以及对客户端进行精心设计的查询而被泄露。然而，量子通信信道被认为更加安全，因为对信道的测量会导致信息损失，且这种损失可被发送方检测到。因此，量子版本的联邦学习可用于提供更强的隐私保护。此外，通过量子信道发送一个N维数据向量需要发送log N个纠缠量子比特，若数据向量被用作量子态，这有可能带来指数级的效率提升。本文提出一种量子联邦学习模型，其中固定设计的量子芯片根据中央服务器发送的量子态运行。客户端基于接收到的叠加态计算本地梯度，并将其作为量子态发送给服务器，服务器聚合这些梯度以更新参数。由于服务器不发送模型参数，而是将算子以量子态形式发送，客户端无需共享模型，从而允许创建异步学习模型。此外，模型以量子态形式直接输入客户端芯片，因此无需测量即将到来的量子态以获取模型参数来计算梯度。与通过经典或量子信道发送参数向量并通过获得的参数值计算本地梯度的模型相比，这能带来效率优势。