A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease
Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s impact and prognosis can be altered significantly. Blood biosamples are often employed in si...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8272 |
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| author | Kasem Khalil Mohammad Mahbubur Rahman Khan Mamun Ahmed Sherif Mohamed Said Elsersy Ahmad Abdel-Aliem Imam Mohamed Mahmoud Maazen Alsabaan |
| author_facet | Kasem Khalil Mohammad Mahbubur Rahman Khan Mamun Ahmed Sherif Mohamed Said Elsersy Ahmad Abdel-Aliem Imam Mohamed Mahmoud Maazen Alsabaan |
| author_sort | Kasem Khalil |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s 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’s 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’ 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. |
| first_indexed | 2024-03-10T21:34:40Z |
| format | Article |
| id | doaj.art-6fcf9ffd108e48e090afe1173324f065 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:40Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6fcf9ffd108e48e090afe1173324f0652023-11-19T15:05:11ZengMDPI AGSensors1424-82202023-10-012319827210.3390/s23198272A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s DiseaseKasem Khalil0Mohammad Mahbubur Rahman Khan Mamun1Ahmed Sherif2Mohamed Said Elsersy3Ahmad Abdel-Aliem Imam4Mohamed Mahmoud5Maazen Alsabaan6Electrical and Computer Engineering Department, University of Mississippi, Oxford, MS 38677, USAElectrical and Computer Engineering Department, Tennessee Technological University, Cookeville, TN 38505, USASchool of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USAComputer Information Systems Department, Higher Colleges of Technology, Al Ain 25026, United Arab EmiratesCollege of Osteopathic Medicine, William Carey University, Hattiesburg, MS 39401, USAElectrical and Computer Engineering Department, Tennessee Technological University, Cookeville, TN 38505, USADepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi ArabiaAlzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s 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’s 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’ 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.https://www.mdpi.com/1424-8220/23/19/8272Alzheimer’s diseasehardware accelerationblood bio-samplesfederated learningearly diagnosis |
| spellingShingle | Kasem Khalil Mohammad Mahbubur Rahman Khan Mamun Ahmed Sherif Mohamed Said Elsersy Ahmad Abdel-Aliem Imam Mohamed Mahmoud Maazen Alsabaan A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease Sensors Alzheimer’s disease hardware acceleration blood bio-samples federated learning early diagnosis |
| title | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
| title_full | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
| title_fullStr | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
| title_full_unstemmed | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
| title_short | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
| title_sort | federated learning model based on hardware acceleration for the early detection of alzheimer s disease |
| topic | Alzheimer’s disease hardware acceleration blood bio-samples federated learning early diagnosis |
| url | https://www.mdpi.com/1424-8220/23/19/8272 |
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