Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Praghash, K.; Peter, Geno; Ananth, Christo; Supriya, M.; Stonier, Albert Alexander; Lawrence, Thomas Samraj Title A name given to the resource Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Distributed Sensor Networks;Volume;2026;Issue;1;Article No.;8463941; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1155/dsn/8463941" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/dsn/8463941</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039690553?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039690553?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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 &amp; Sons Ltd. Subject The topic of the resource edge computing; federated learning; resource allocation; smart healthcare; sustainable healthcare; swarm intelligence Publisher An entity responsible for making the resource available John Wiley and Sons Ltd Relation A related resource ISSN: 15501329; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online