Quantum delegated and federated learning via quantum homomorphic encryption
This work addresses data privacy concerns for clients using quantum cloud services, offering a theoretical guarantee, but it appears incremental as it builds on existing quantum encryption and learning methods.
The paper tackles the problem of protecting clients' private data in quantum learning on the cloud by proposing a framework using quantum homomorphic encryption, which reduces communication complexity compared to blind quantum computing and lowers computational burden on local devices in federated learning.
Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients' private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.