Abstract
This research work describes a lightweight, secure, and interpretable federated learning framework for automatic leukemia classification, which identifies and addresses various problems regarding clinical data security and collaborative model building among partnering healthcare organizations. This framework employs a distributed learning paradigm that allows a number of healthcare facilities to work together to build a high predictive performance classification model while training the model without exchanging sensitive information about patient data, thus ensuring data privacy and methodological reproducibility. The proposed framework employs a lightweight attention-enhanced convolutional neural network (CNN) for the automated classification of leukemia cells to one of the four categories: benign, early, pre-leukemic, and pro-leukemic at only 0.14 s/batch. The global model at 3 clients achieves 95.70% test accuracy while at 5 clients and increased training rounds achieve 96.56% on test set on a weighted aggregation method. Additionally, for increased clinical interpretability and transparency explainable methods are used in this study.
Data availability
The dataset used in this study is publicly available on Kaggle and was provided by Aria, M., Ghaderzadeh, M., Bashash, D., Abolghasemi, H., Asadi, F., and Hosseini, A. (2021). It is titled Acute Lymphoblastic Leukemia (ALL) Image Dataset and can be accessed via the following DOI:10.34740/KAGGLE/DSV/2175623.
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Funding
This article has been produced with the financial support of the European Union under the LERCO project number \(CZ.10.03.01/00/22\_003/0000003\) via the Operational Programme Just Transition. This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).
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The authors confirm their contribution to the paper as follows: Conceptualization: MZA,NAK, and AJ; Methodology: MZA, NAK, and AJ; Software: PS, LR; Validation: AJ, PS; Formal analysis, MZA, and NAK; Investigation: MZA, PS; Resources: LR; Data Curation: MZA and NAK; Writing—original draft preparation: MZA,NAK, and PS; writing—review and editing: AJ and PS; Visualization: MZA; Supervision: LR; Project administration: AJ and LR; Funding acquisition: LR. All authors reviewed the results and approved the final version of the manuscript.
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Awan, M.Z., Khan, N.A., Strakos, P. et al. Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40581-9
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DOI: https://doi.org/10.1038/s41598-026-40581-9
