ItemArticle

Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks

Title
Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks
Creator
Praghash, K.; Peter, Geno; Ananth, Christo; Supriya, M.; Stonier, Albert Alexander; Lawrence, Thomas Samraj
Description
Smart healthcare edge networks should be able to serve two purposes at once: to train federated machine learning models across a range of devices without violating patient privacy and to schedule other activities with latency constraints, like real-time patient events. Such methods as FFL-ANN attempt this by using fixed fuzzy rules, which do not work in the situation where the conditions of the network change in an unforeseen manner. In this paper, the framework FSI-ANN is introduced to combine particle swarm optimization to quality-aware model aggregation with ant colony optimization to adaptive real-time task scheduling and ANN-based predictions into a single framework. We experimented with FSI-ANN on 200 edge devices. It achieved 0.825 precision compared with 0.82 with FedAvg and 0.80 with FFL-ANN and reduced inference latency by 18%, 0.370.45 s. Throughput was maintained at 33 tasks/sec as compared with 27 of FedAvg. At burst load, the miss rate of the critical deadline was decreased by 90.2 percent and the energy consumed was decreased by 14.8% per round. The results suggest that adaptive learning using swarm is superior to the fixed rule-based approaches and simple averaging in the distribution of resources at the sustainable healthcare advantage. Copyright 2026 K. Praghash et al. International Journal of Distributed Sensor Networks published by John Wiley & Sons Ltd.
Source
International Journal of Distributed Sensor Networks;Volume;2026;Issue;1;Article No.;8463941;
Date
01-01-2026
Publisher
John Wiley and Sons Ltd
Subject
edge computing; federated learning; resource allocation; smart healthcare; sustainable healthcare; swarm intelligence
Coverage
Praghash K., Department of Electronics and Communications Engineering, Christ University, Bengaluru, India; Peter G., Centre for Research of Innovation and Sustainable Development (CRISD), School of Engineering and Technology, University of Technology Sarawak, Sibu, Malaysia; Ananth C., Faculty of Artificial Intelligence and Digital Technologies, Samarkand State University, Samarkand, Uzbekistan; Supriya M., Department of Computer Science Engineering, Stella Mary?s College of Engineering, Nagercoil, India; Stonier A.A., School of Electrical Engineering, Vellore Institute of Technology, Vellore, India; Lawrence T.S., Department of Information Technology, School of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Ethiopia
Rights
All Open Access; Gold Open Access
Relation
ISSN: 15501329;
Format
online
Language
English
Type
Article

Citation

Praghash, K.; Peter, Geno; Ananth, Christo; Supriya, M.; Stonier, Albert Alexander; Lawrence, Thomas Samraj, “Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23061.

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