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Such methods as FFL\u2010ANN 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\u2010ANN is introduced to combine particle swarm optimization to quality\u2010aware model aggregation with ant colony optimization to adaptive real\u2010time task scheduling and ANN\u2010based predictions into a single framework. We experimented with FSI\u2010ANN on 200 edge devices. It achieved 0.825 precision compared with 0.82 with FedAvg and 0.80 with FFL\u2010ANN and reduced inference latency by 18%, 0.37\u20130.45\u2009s. 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\u2010based approaches and simple averaging in the distribution of resources at the sustainable healthcare advantage.<\/jats:p>","DOI":"10.1155\/dsn\/8463941","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:15:37Z","timestamp":1779275737000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural 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