Document Type

Article

Publication Date

2026

DOI

10.1038/s41598-026-45454-9

Publication Title

Scientific Reports

Volume

Advance online publication

Pages

27 pp.

Abstract

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly outperforms existing federated knowledge distillation methods. It achieves a generalization performance with accuracy up to 82.79% and an F1-score of 82.78% on Non-IID data. Among the DP methods, the Tree Adaptive mechanism has consistently provided the best trade-off between privacy and prediction quality. Peak accuracy reaches 76.62% under strict privacy constraints, where ϵ = 3.90 and ẟ = 10-³. This result is close to the performance of models without privacy protections. These findings demonstrate that adaptive DP techniques can be effectively applied in federated healthcare models. They support the development of privacy-preserving AI systems for clinical diagnostics.

Rights

© The Authors 2026.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article States: "All project codes are provided at GitHub QinggeLab-FLKD-DP. The data was provided at COVID-19 CT Scan Dataset 1, COVID-19 CT Scan Dataset 2, COVID-19 CT Scan Dataset 3, and COVID-19 CT Scan Dataset 4."

Original Publication Citation

Annan, R., Qin, H., Newman, R., Siddula, M., & Qingge, L. (2026). Privacy-preserving federated learning with optimized ensemble weighting and knowledge distillation for COVID-19 detection from Non-IID medical imaging data. Scientific Reports. Advance online publication. https://doi.org/10.1038/s41598-026-45454-9

ORCID

0000-0002-1060-6722 (Qin)

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