A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

Nidal Nasser; Zubair Md Fadlullah; Mostafa M Fouda; Asmaa Ali; Muhammad Imran

doi:10.1016/j.comnet.2021.108672

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

Comput Netw. 2022 Mar 14:205:108672. doi: 10.1016/j.comnet.2021.108672. Epub 2021 Dec 20.

Authors

Nidal Nasser¹, Zubair Md Fadlullah^{2

3}, Mostafa M Fouda^{4

5}, Asmaa Ali⁶, Muhammad Imran^{7

8}

Affiliations

¹ College of Engineering, Alfaisal University, Riyadh, Saudi Arabia.
² Department of Computer Science, Lakehead University, Thunder Bay, P7B 5E1, ON, Canada.
³ Research Institute (RI), Thunder Bay Regional Health Research Institute (TBRHRI), Thunder Bay, P7B 6V4, ON, Canada.
⁴ Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, 83209, Idaho, USA.
⁵ Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt.
⁶ School of Computing, Queens' University, Kingston, K7L 2N8, ON, Canada.
⁷ School of Engineering, Information Technology and Physical Sciences,, Federation University, Brisbane, Australia.
⁸ College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

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.

Keywords: 5G; Artificial intelligence (AI); Beyond 5G (B5G); Edge computing; Federated learning; Pandemic; Unmanned aerial vehicle (UAV).