{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:55:49Z","timestamp":1790960149855,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSPD2023R636"],"award-info":[{"award-number":["RSPD2023R636"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a progressive illness with a slow start that lasts many years; the disease\u2019s consequences are devastating to the patient and the patient\u2019s family. If detected early, the disease\u2019s impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer\u2019s disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices\u2019 raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.<\/jats:p>","DOI":"10.3390\/s23198272","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T08:29:12Z","timestamp":1696580952000},"page":"8272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9659-8566","authenticated-orcid":false,"given":"Kasem","family":"Khalil","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Mississippi, Oxford, MS 38677, USA"},{"name":"Department of Electrical Engineering, Assiut University, Assiut 71515, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6369-7915","authenticated-orcid":false,"given":"Mohammad Mahbubur Rahman","family":"Khan Mamun","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Tennessee Technological University, Cookeville, TN 38505, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1651-7325","authenticated-orcid":false,"given":"Ahmed","family":"Sherif","sequence":"additional","affiliation":[{"name":"School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6533-2514","authenticated-orcid":false,"given":"Mohamed Said","family":"Elsersy","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, Higher Colleges of Technology, Al Ain 25026, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Abdel-Aliem","family":"Imam","sequence":"additional","affiliation":[{"name":"College of Osteopathic Medicine, William Carey University, Hattiesburg, MS 39401, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Tennessee Technological University, Cookeville, TN 38505, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8601-3184","authenticated-orcid":false,"given":"Maazen","family":"Alsabaan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14474","DOI":"10.1109\/JSEN.2020.3008063","article-title":"Sensor signals-based early dementia detection system using travel pattern classification","volume":"20","author":"Chaudhary","year":"2020","journal-title":"IEEE Sens. 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