Quantum Enhanced Federated Learning with Differential Privacy
S. M. Wazid Ullah, Muzammil Shah, Adeel Anjum · 2024
Quantum Federated Learning (QFL) integrates the principles of quantum mechanics with classical machine learning to address the privacy challenges in distributed learning settings. This study introduces a Quantum Enhanced Federated Learning framework with Differential Privacy (QE-FLDP), designed to enhance privacy protections by leveraging quantum mechanics principles. The framework uses a quantum feature engineering circuit that encodes classical data into high-dimensional quantum states through parameterized quantum gates, including Rx rotations and CNOT gates. To ensure privacy, Differential Privacy is incorporated via the Laplace mechanism to safeguard data during the training process. Additionally, the framework integrates Quantum Homomorphic Encryption (QHE), enabling secure computations on encrypted data without exposing the underlying information. Model updates are transmitted over quantum channels post-training, preserving data integrity and ensuring compliance with privacy standards. The QE-FLDP framework was evaluated on synthetic datasets, achieving an average Mean Squared Error (MSE) of 0.0143 and an R2 score of 0.987 over 100 training rounds. These results highlight the potential of quantum principles to improve both model accuracy and data privacy in distributed learning settings.