TY  - EJOU
AU  - Hamdi, Hedi 
AU  - Brahmi, Zaki 
AU  - Lajili, Sabeur 
AU  - Almashfi, Nabil 

TI  - Federated Learning–Driven Trustful and Privacy-Preserving Service Composition in Virtual Hospitals
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 148
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Federated learning; service composition; srust management; srivacy preservation; sirtual hospitals; internet of medical things (IoMT)

DO  - 10.32604/cmes.2026.083334