CryptoQFL: Quantum Federated Learning on Encrypted Data

TL;DR

CryptoQFL introduces a secure, communication-efficient, and computationally optimized quantum federated learning framework that enables training on encrypted data while preserving privacy and reducing latency.

Contribution

It is the first framework to combine quantum federated learning with encrypted data, ensuring privacy, efficiency, and reduced communication and computation costs.

Findings

Secure quantum federated learning with encrypted data demonstrated.

Reduced communication overhead via ternary gradient quantization.

Quantum aggregation circuit significantly lowers latency.

Abstract

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.