{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:04:33Z","timestamp":1790229873799,"version":"4.1.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>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\u2013classical federated learning framework using a frozen four-qubit quanvolutional front-end. The 128 \u00d7 128 grayscale MRI images are divided into 2 \u00d7 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\u201330 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 \u03f5 = 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.<\/jats:p>","DOI":"10.3389\/fnins.2026.1904876","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:44:45Z","timestamp":1790228685000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Alzheimer's detection using quanvolutional neural networks with federated training"],"prefix":"10.3389","volume":"20","author":[{"given":"Prakhar","family":"Mishra","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subitha","family":"D.","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pramukh Rajendra","family":"Jain","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arogya","family":"Sharma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavitha","family":"J. C.","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"045032","DOI":"10.1088\/2058-9565\/acfc61","article-title":"Federated quanvolutional neural network: a new paradigm for collaborative quantum learning","volume":"8","author":"Bhatia","year":"2023","journal-title":"Quant. Sci. Technol"},{"key":"B2","first-page":"10913485","article-title":"\u201cCommunication-efficient quantum federated learning optimization for multi-center healthcare data,\u201d","volume-title":"Proc 2024 IEEE EMBS int conf on biomedical and health informatics (BHI)","author":"Bhatia","year":"2024"},{"key":"B3","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2104.07046","article-title":"Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning","author":"Boev","year":"2021","journal-title":"arXiv Preprint arXiv:2104.07046"},{"key":"B5","first-page":"1","article-title":"\u201cTowards federated learning at scale: system design,\u201d","volume-title":"Proc 2nd SysML Conference","author":"Bonawitz","year":"2019"},{"key":"B6","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","article-title":"Variational quantum algorithms","volume":"3","author":"Cerezo","year":"2021","journal-title":"Nat Rev Phys"},{"key":"B7","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/TWC.2020.3024629","article-title":"joint learning and communications framework for federated learning over wireless networks","volume":"20","author":"Chen","year":"2021","journal-title":"IEEE Trans. Wirel. Commun"},{"key":"B8","doi-asserted-by":"publisher","first-page":"460","DOI":"10.3390\/e23040460","article-title":"Federated quantum machine learning","volume":"23","author":"Chen","year":"2021","journal-title":"Entropy"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","article-title":"Quantum convolutional neural networks","volume":"15","author":"Cong","year":"2019","journal-title":"Nat. Phys"},{"key":"B10","doi-asserted-by":"publisher","first-page":"064401","DOI":"10.1063\/5.0138021","article-title":"An improved hybrid quantum-classical convolutional neural network for multi-class brain tumor MRI classification","volume":"133","author":"Dong","year":"2023","journal-title":"J. Appl. Phys"},{"key":"B11","unstructured":"Dubey\n              S.\n            \n          \n          \n          2019"},{"key":"B12","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","article-title":"\u201cCalibrating noise to sensitivity in private data analysis,\u201d","author":"Dwork","year":"2006","journal-title":"Theory of cryptography conference (TCC)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends Theoret. Comput. Scie"},{"key":"B14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","article-title":"Supervised learning with quantum-enhanced feature spaces","volume":"567","author":"Havl\u00ed\u010dek","year":"2019","journal-title":"Nature"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1904.04767","article-title":"Quanvolutional neural networks: powering image recognition with quantum circuits","author":"Henderson","year":"2019","journal-title":"arXiv Preprint arXiv:1904.04767 [Preprint"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1610.05492","article-title":"Federated learning: strategies for improving communication efficiency","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv Preprint arXiv:1610.05492 [Preprint"},{"key":"B17","unstructured":"Kumar\n              S.\n            \n          \n          Alzheimer MRI Preprocessed Dataset\n          \n          2024"},{"key":"B18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process Mag"},{"key":"B19","doi-asserted-by":"publisher","first-page":"290311","DOI":"10.1007\/s11433-021-1734-3","article-title":"Hybrid quantum-classical convolutional neural networks","volume":"64","author":"Liu","year":"2021","journal-title":"Sci. China Phys. Mech. Astron"},{"key":"B20","doi-asserted-by":"publisher","first-page":"340","DOI":"10.22331\/q-2020-10-09-340","article-title":"Transfer learning in hybrid classical-quantum neural networks","volume":"4","author":"Mari","year":"2020","journal-title":"Quantum"},{"key":"B21","first-page":"1273","article-title":"\u201cCommunication-efficient learning of deep networks from decentralized data,\u201d","volume-title":"Proc 20th int conf artificial intelligence and statistics (AISTATS)","author":"McMahan","year":"2017"},{"key":"B22","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/s41746-021-00489-2","article-title":"Privacy-first health research with federated learning","volume":"4","author":"Sadilek","year":"2021","journal-title":"NPJ Dig. Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","article-title":"Robust and communication-efficient federated learning from non-IID data","volume":"31","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B24","doi-asserted-by":"publisher","first-page":"040504","DOI":"10.1103\/PhysRevLett.122.040504","article-title":"Quantum machine learning in feature Hilbert spaces","volume":"122","author":"Schuld","year":"2019","journal-title":"Phys. Rev. Lett"},{"key":"B25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-030-11723-8_9","volume":"11383","author":"Sheller","year":"2019"},{"key":"B26","doi-asserted-by":"publisher","first-page":"025019","DOI":"10.1088\/2058-9565\/abe107","article-title":"Adaptive pruning-based optimization of parameterized quantum circuits","volume":"6","author":"Sim","year":"2021","journal-title":"Quant. Sci. Technol"},{"key":"B27","doi-asserted-by":"publisher","first-page":"045035","DOI":"10.1088\/2632-2153\/ad8c11","article-title":"Federated learning with tensor networks: a quantum AI framework for healthcare","volume":"5","author":"Singh Bhatia","year":"2024","journal-title":"Mach. Learn.: Sci. Technol"},{"key":"B28","unstructured":"A Hybrid Quantum-Classical Machine Learning Library\n          \n          2020"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2007.07481","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization (FedNova)","author":"Wang","year":"2020","journal-title":"arXiv Preprint arXiv:2007.07481"},{"key":"B30","unstructured":"Yang\n              T.\n            \n            \n              Andrew\n              G.\n            \n            \n              McMahan\n              H. B.\n            \n          \n          Applied Federated Learning: Improving Google Keyboard Query Suggestions\n          \n          2018"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3185327","article-title":"\u201cHomomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System,\u201d","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Network Science and Engineering, vol. 10"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1806.00582","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv [Preprint] arXiv:1806.00582"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2026.1904876\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:44:47Z","timestamp":1790228687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2026.1904876\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":32,"alternative-id":["10.3389\/fnins.2026.1904876"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2026.1904876","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]},"article-number":"1904876"}}