  Recent advancements in Quantum Neural Networks (QNNs) have demonstrated
theoretical and experimental performance superior to their classical
counterparts in a wide range of applications. However, existing centralized
QNNs cannot solve many real-world problems because collecting large amounts of
training data to a common public site is time-consuming and, more importantly,
violates data privacy. Federated Learning (FL) is an emerging distributed
machine learning framework that allows collaborative model training on
decentralized data residing on multiple devices without breaching data privacy.
Some initial attempts at Quantum Federated Learning (QFL) either only focus on
improving the QFL performance or rely on a trusted quantum server that fails to
preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that
allows distributed QNN training on encrypted data. CryptoQFL is (1) secure,
because it allows each edge to train a QNN with local private data, and encrypt
its updates using quantum \homo~encryption before sending them to the central
quantum server; (2) communication-efficient, as CryptoQFL quantize local
gradient updates to ternary values, and only communicate non-zero values to the
server for aggregation; and (3) computation-efficient, as CryptoQFL presents an
efficient quantum aggregation circuit with significantly reduced latency
compared to state-of-the-art approaches.
