Open Access

ARTICLE

Federated Learning–Driven Trustful and Privacy-Preserving Service Composition in Virtual Hospitals

Hedi Hamdi^1,*, Zaki Brahmi², Sabeur Lajili³, Nabil Almashfi⁴

1 Department of Computer Science, Jouf University, Sakaka, Saudi Arabia
2 Department of Software & IT Engineering, ETS Montreal, University of Quebec, Montreal, QC, Canada
3 Department of Computer Science, ISITCOM, University of Sousse, Sousse, Tunisia
4 Department of Software Engineering, Jouf University, Sakaka, Saudi Arabia

* Corresponding Author: Hedi Hamdi. Email:

Computer Modeling in Engineering & Sciences 2026, 148(3), 50 https://doi.org/10.32604/cmes.2026.083334

Received 02 April 2026; Accepted 25 June 2026; Issue published 28 September 2026

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.

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Keywords

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