Leveraging federated learning and edge computing for pandemic-resilient healthcare

• Published in: Scientific reports Vol. 15; no. 1; pp. 20497 - 23
• Main Authors: Choudhury, Atlanta, Sarma, Kandarpa Kumar, Gulvanskii, Vyacheslav, Kaplun, Dmitrii, Dutta, Lachit
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 639/705/1042; 639/705/117; 692/700/478/174; Artificial Intelligence; Contact tracing; Contact Tracing - methods; Deep Learning; Deep transfer learning; Delivery of Health Care; Edge computing; Facemask; Federated Learning; Humanities and Social Sciences; Humans; Internet of Things; multidisciplinary; Pandemics - prevention & control; Science; Science (multidisciplinary); Social distancing; Deep learning; Federated learning; Social distancing; Contact tracing; Facemask; Cyber-attack; Deep transfer learning; Edge computing
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-00199-9

The universal demand for the development and deployment of responsive medical infrastructure and damage control techniques, including the application of technology, is the foremost necessity that emerged immediately in the post-pandemic era. Numerous technologies, such as artificial intelligence (AI)-aided decision-making and the Internet of Things (IoT), have been rendered indispensable for such applications. Federated learning (FL) is a popular approach used to enhance AI-driven decision support systems and maintain decentralized learning. As part of a bio-safety norms observance setup, IoT, edge computing, and FL tools can be configured to monitor social distance norms, face-mask use, contact tracing, and cyber-attacks. The design of a pandemic-compliant mechanism for keeping an eye on protocol observance of virus-triggered infectious disease and contact tracing is the subject of this study. The mechanism is based on edge computing, FL frameworks, and a variety of sensors that are connected via IoT. We employ a variety of deep learning pre-trained models (DPTM) as benchmark techniques to compare the performance of the proposed YOLOv4 and SENet attention layer combination. This combination is deployed on a FL framework that is executed using a server and Grove AI-Raspberry Pi 4 blocks act as nodes as part of a human residential premises. The models include the RESNET-50, MobileNetV2, and SocialdistancingNet-19. In particular, the integration of the YoloV4 and SENET attention layer as part of a FL framework delivers dependable performance while addressing facemask detection (94.6%), incorrect facemask detection (98%), facemask classification (95.4%), social distance (96.1%), contact tracing (95.2%) and cyber attack detection (94.2%) while performing tasks like correct and incorrect, proper and improper facemask wearing, monitoring social distancing norms observance, and contact tracing.