Alzheimer's detection using quanvolutional neural networks with federated training

Abstract

Alzheimer's disease detection from MRI data is challenging because of subtle anatomical patterns, high-dimensional image data, and privacy constraints associated with centralized medical-data training. We propose a hybrid quantum–classical federated learning framework using a frozen four-qubit quanvolutional front-end. The 128 × 128 grayscale MRI images are divided into 2 × 2 patches, and quantum features are pre-extracted using a fixed circuit implemented in PennyLane. The resulting features are classified using trainable classical dense layers implemented in TensorFlow/Keras. Federated training is performed across five non-IID clients using FedAvg and FedNova over ten communication rounds, with dynamic local epochs of 5–30 and batch size 32. Expectation-Value Local Differential Privacy (EV-LDP) is also evaluated. The proposed FedNova-DQ framework achieved a final global accuracy of 0.923, compared with 0.921 using FedAvg. The centralized QCNN achieved 0.891 accuracy compared with 0.872 for the classical CNN baseline. Under EV-LDP with ϵ = 2.0, the model maintained 90.85% accuracy. At K = 200 clients, FedNova achieved 90.15% accuracy compared with 82.15% for FedAvg. The frozen quantum front-end eliminates quantum-parameter communication and decouples quantum feature extraction from federated communication rounds while maintaining strong classification performance under heterogeneous client distributions.