Alzheimer's detection using quanvolutional neural networks with federated training

Overview

1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/rajmohan096, at Kaggle; found in the references
• kaggle.com/datasets/sachinkumar413, at Kaggle; found in the references
• kaggle.com/datasets/tourist55, at Kaggle; found in the references

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 9 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 25 references.

Cite

This paper

Mishra, P., D., S., Jain, P. R., Sharma, A., & J. C., K. (2026). Alzheimer's detection using quanvolutional neural networks with federated training. Frontiers in neuroscience, 20, 1904876.

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