Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks.
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| Title: | Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks. |
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| Authors: | Praghash, K.1 (AUTHOR), Peter, Geno2 (AUTHOR) genopeter@gmail.com, Ananth, Christo3 (AUTHOR), Supriya, M.4 (AUTHOR), Stonier, Albert Alexander5 (AUTHOR), Lawrence, Thomas Samraj6 (AUTHOR) samraj@dadu.edu.et, Botzheim, Janos (AUTHOR) dr.janos.botzheim@ieee.org |
| Source: | International Journal of Distributed Sensor Networks. 5/20/2026, Vol. 2026, p1-29. 29p. |
| Subjects: | Swarm intelligence, Artificial neural networks, Communication infrastructure, Particle swarm optimization, Scheduling, Federated learning, Digital health, Resource allocation |
| Abstract: | 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%, 0.37–0.45 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | 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%, 0.37–0.45 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15501329 |
| DOI: | 10.1155/dsn/8463941 |
