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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.
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]
Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Distributed+Sensor+Networks%22">International Journal of Distributed Sensor Networks</searchLink>. 5/20/2026, Vol. 2026, p1-29. 29p.
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  Data: <searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+infrastructure%22">Communication infrastructure</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+health%22">Digital health</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1155/dsn/8463941
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      – Code: eng
        Text: English
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        PageCount: 29
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      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Communication infrastructure
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Federated learning
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      – SubjectFull: Digital health
        Type: general
      – SubjectFull: Resource allocation
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      – TitleFull: Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks.
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              M: 05
              Text: 5/20/2026
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              Y: 2026
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              Value: 2026
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