Cargando…
A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept
The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) netw...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702301/ https://www.ncbi.nlm.nih.gov/pubmed/35023995 http://dx.doi.org/10.1016/j.comnet.2021.108672 |
_version_ | 1784621216026329088 |
---|---|
author | Nasser, Nidal Fadlullah, Zubair Md Fouda, Mostafa M. Ali, Asmaa Imran, Muhammad |
author_facet | Nasser, Nidal Fadlullah, Zubair Md Fouda, Mostafa M. Ali, Asmaa Imran, Muhammad |
author_sort | Nasser, Nidal |
collection | PubMed |
description | The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. |
format | Online Article Text |
id | pubmed-8702301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87023012021-12-28 A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept Nasser, Nidal Fadlullah, Zubair Md Fouda, Mostafa M. Ali, Asmaa Imran, Muhammad Comput Netw Article The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. Elsevier B.V. 2022-03-14 2021-12-20 /pmc/articles/PMC8702301/ /pubmed/35023995 http://dx.doi.org/10.1016/j.comnet.2021.108672 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Nasser, Nidal Fadlullah, Zubair Md Fouda, Mostafa M. Ali, Asmaa Imran, Muhammad A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title | A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title_full | A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title_fullStr | A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title_full_unstemmed | A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title_short | A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept |
title_sort | lightweight federated learning based privacy preserving b5g pandemic response network using unmanned aerial vehicles: a proof-of-concept |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702301/ https://www.ncbi.nlm.nih.gov/pubmed/35023995 http://dx.doi.org/10.1016/j.comnet.2021.108672 |
work_keys_str_mv | AT nassernidal alightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT fadlullahzubairmd alightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT foudamostafam alightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT aliasmaa alightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT imranmuhammad alightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT nassernidal lightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT fadlullahzubairmd lightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT foudamostafam lightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT aliasmaa lightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept AT imranmuhammad lightweightfederatedlearningbasedprivacypreservingb5gpandemicresponsenetworkusingunmannedaerialvehiclesaproofofconcept |