{
  "abstract": "Healthcare Internet of Things (IoT) devices generate vast amounts of sensitive patient data, creating significant privacy risks and regulatory challenges when processed through centralized architectures. Federated Learning (FL) offers a promising solution by enabling decentralized model training without sharing raw data. However, real-world deployment faces critical challenges including device heterogeneity, intermittent connectivity, non-independent and identically distributed (non-IID) data, and unreliable node participation. This paper proposes a Reliability-Aware Adaptive Aggregation (RAAA) framework that dynamically evaluates client trustworthiness through three dimensions: update quality (local accuracy and gradient alignment), participation consistency (historical engagement patterns), and communication latency. These metrics are integrated into a composite reliability score using weighted aggregation, which adaptively prioritizes contributions from trustworthy nodes during global model updates. The framework was validated using PhysioNet electrocardiogram (ECG) datasets (MIT-BIH and PTB) with 20 simulated healthcare IoT clients under conditions of node dropout and data heterogeneity. Experimental results demonstrate that RAAA achieved 91.7% classification accuracy on arrhythmia detection, outperforming Federated Averaging (FedAvg) (85.2%), Federated Proximal (FedProx) (86.4%), and Q-FedAvg (87.1%) by 6.5%, 5.3%, and 4.6%, respectively. The proposed method converged 17% faster (43 versus 52 rounds) and maintained an 85.6% client participation rate. Cross-dataset validation on the PTB database confirmed consistent improvements across different physiological signals. The RAAA framework provides a robust, privacy-preserving FL solution for healthcare IoT environments with superior accuracy, faster convergence, and resilience to unreliable nodes. All evaluations are conducted in a simulated environment; while theoretical energy and complexity analyses suggest suitability for resource-constrained edge devices, real-hardware validation remains an important direction for future work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Vishal Barot"
    },
    {
      "name": "Jashvant Dave"
    },
    {
      "name": "Manoj R."
    },
    {
      "name": "Safvan Vahora"
    },
    {
      "name": "Nilesh Maltare"
    },
    {
      "name": "Jayesh Gangrade"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/avhgsydqej2ff.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-69453-y",
  "fulltext_url": null,
  "id": "avhgsydqej2ff",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-69453-y",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7221913954",
  "pdf_url": "https://www.nature.com/articles/s41598-026-69453-y.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/avhgsydqej2ff.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.1038/s41598-026-69453-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-69453-y"
    }
  ],
  "subjects": [],
  "title": "Federated learning with adaptive reliability aware aggregation for privacy preserving healthcare IoT systems",
  "title_text": "Federated learning with adaptive reliability aware aggregation for privacy preserving healthcare IoT systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/avhgsydqej2ff",
  "venue": "Scientific Reports",
  "versions": []
}