- Title
- A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept
- Creator
- Nasser, Nasser; Fadlullah, Zubair; Fouda, Mostafa; Ali, Asmaa; Imran, Muhammad
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186680
- Identifier
- vital:16928
- Identifier
-
https://doi.org/10.1016/j.comnet.2021.108672
- Identifier
- ISBN:1389-1286 (ISSN)
- Abstract
- 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. © 2021 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Computer Networks Vol. 205, no. (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 Elsevier B.V.
- Subject
- 40 Engineering; 46 Information and computing sciences; 5G; Artificial intelligence (AI); Beyond 5G (B5G); Edge computing; Federated learning; Pandemic; Unmanned aerial vehicle (UAV)
- Reviewed
- Funder
- Authors would like to thank Alfaisal University, Saudi Arabia for funding and supporting this work through the Internal Research Grant IRG21207.
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