Journal of Smart Computing ISSN 2979-8752 · Biannual
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Research Article · Vol. 2, No. 1 (2026)

Federated Learning with Differential Privacy for Healthcare IoT: A Privacy-Preserving Framework for Collaborative Clinical Modeling

Esmaeil Shayesteh , Vasileios Lymperopoulos*

Department of Industrial Engineering, CT.C., Islamic Azad University, Tehran · CEO, LiberoGroup, Greece

* Corresponding author: vasileios.lymperopoulos@liberoservices.org

Abstract Wearable sensors and connected medical devices have made healthcare Internet of Things (IoT) systems a rich and highly sensitive source of clinical data. Centralizing these data for model training raises privacy, regulatory, and security concerns. This paper proposes a privacy-preserving framework that combines federated learning with client-level differential privacy, enabling hospitals and device silos to train a shared clinical model without exchanging raw patient records. Clients train locally; their updates are clipped and perturbed with calibrated Gaussian noise, and only the protected updates are aggregated by a central server using federated averaging. We formalize the privacy accounting, present the training procedure as pseudocode, and evaluate the framework on a simulated non-independent and identically distributed (non-IID) healthcare dataset spanning ten clients. The experiments map the trade-off between the privacy budget and predictive utility, and show that strong privacy guarantees can be achieved at a modest, controllable cost to accuracy. The results indicate that differentially private federated learning is a viable approach for edge-based healthcare IoT settings in which utility, security, and compliance must be balanced.

Keywords: Federated learning, Differential privacy, Healthcare IoT, Privacy-preserving Machine Learning, Edge computing

DOI
10.5281/zenodo.23257468
Pages
1-10
Published
5 Feb 2026
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To cite this article: Shayesteh, E., & Lymperopoulos, V. (2026). Federated Learning with Differential Privacy for Healthcare IoT: A Privacy-Preserving Framework for Collaborative Clinical Modeling. Journal of Smart Computing , 2(1) , 1-10. https://doi.org/10.5281/zenodo.23257468

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