Leveraging federated learning and edge computing for pandemic-resilient healthcare

Abstract

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.

Keywords: Contact tracing; Cyber-attack; Deep learning; Deep transfer learning; Edge computing; Facemask; Federated learning; Social distancing.

Fig. 1

Functioning of the SENet attention mechanism.

Fig. 2

YOLOv4 and SENet attention layer configured for the proposed approach.

Fig. 3

Block diagram showing different attributes and components of the proposed approach.

Fig. 4

FL-aided pandemic protocol observance monitoring system for a residential apartment.

Fig. 5

Proposed IoHT based on FL designed for pandemic compliant infrastructure.

Algorithm 1

Federated learning-based process logic for training nodes and the cloud server

Fig. 6

Examples of face masks datasets with masks.

Fig. 7

Examples of face masks datasets without mask.

Fig. 8

Examples of face masks datasets incorrect face mask.

Fig. 9

Datasets of (a) NG facemasks and (b) medical facemask.

Fig. 10

Accuracy performance recorded by federated learning v/s centralized learning.

Fig. 11

Average training and validation accuracy of the YOLOV4-SENet combination while executing protocol observance monitoring.

Fig. 12

Accuracy performance recorded by federated learning v/s centralized learning.

Fig. 13

Federated learning accuracy with facemask recognition while node numbers.