@Article{cmes.2026.083334,
AUTHOR = {Hedi Hamdi, Zaki Brahmi, Sabeur Lajili, Nabil Almashfi},
TITLE = {Federated Learning–Driven Trustful and Privacy-Preserving Service Composition in Virtual Hospitals},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v148n3/68963},
ISSN = {1526-1506},
ABSTRACT = {The increasing adoption of virtual hospitals and Internet of Medical Things (IoMT)-driven clinical workflows demands service composition mechanisms that can operate reliably under strict privacy constraints and heterogeneous provider ecosystems. Traditional trust-based composition approaches, such as majority voting or Bayesian aggregation, struggle to remain robust in large-scale, non-Independent and Identically Distributed (IID), and privacy-sensitive environments. This paper addresses these limitations by introducing PSCP-FL, a Federated Learning–Driven Privacy- and Trust-Aware Service Composition Framework for cloud- and edge-enabled virtual hospitals. We first define a comprehensive set of medical service trust attributes and construct a Federated Learning–based Trust Network (FLTN) that predicts provider trust while preserving data locality. FLTN enables the composer to prune untrustworthy services and select optimal compositions using a federated, dynamically updated evaluation model. Experiments demonstrate that PSCP-FL outperforms the Majority Voting and Bayesian Average-Based Trustful Service Composition (MVBA-TSC) baseline in standard deviation reduction (up to 40%), trust accuracy, robustness under non-IID and adversarial conditions, and execution time (15%–30% faster), while producing more stable and homogeneous service chains. These results confirm that PSCP-FL provides a reliable and privacy-preserving foundation for the composition of critical virtual hospital services.},
DOI = {10.32604/cmes.2026.083334}
}