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MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services
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  • Published: 11 February 2026

MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services

  • Dileep Kumar Murala1,
  • Lavanya Vemulapalli2,
  • Yadaiah Balagoni3,
  • Eswar Patnala4 &
  • …
  • Bananeza Romeo5 

Scientific Reports , Article number:  (2026) Cite this article

  • 550 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Health care
  • Mathematics and computing
  • Medical research

Abstract

The fast growth of telemedicine has made it clear that we need safe, cooperative, and privacy-protecting ways to handle sensitive medical information. Traditional centralised predictive model training methods have problems including data leaks, ownership disputes, and tight rules that make it hard for institutions to work together. To solve these problems, this paper suggests MedLedgerFL, a hybrid platform that combines blockchain with federated learning (FL) to make remote healthcare analytics safe and reliable. The blockchain layer makes sure that the model updates can be audited, can’t be changed, and can only be accessed by authorised healthcare institutions. The federated learning layer lets healthcare institutions work together to train the model without sharing patient data, which makes sure that they follow data protection laws like GDPR. Tests show that MedLedgerFL is better than other methods at making accurate predictions, communicating quickly, and keeping information private. Combining blockchain consensus with federated model aggregation makes things more open, lowers the danger of data exposure, and makes models more reliable. Future endeavours will concentrate on enhancing scalability among institutions, integrating sophisticated privacy-preserving techniques like differential privacy and homomorphic encryption, and assessing the framework’s efficacy in extensive, practical telemedicine applications.

Data availability

The datasets used in this study are publicly available chest X-ray datasets and were not generated by the authors. Specifically, we used COVID-19, pneumonia, tuberculosis (TB), and normal chest X-ray images from the following repositories: 1. NIH COVIDx (https://www.kaggle.com/datasets/andyczhao/covidx-cxr2), 2. ChestX-ray14 (https://nihcc.app.box.com/v/ChestXray-NIHCC), 3. CheXpert (https://stanfordmlgroup.github.io/competitions/chexpert/), and 4. Pulmonary Tuberculosis Dataset (https://www.kaggle.com/datasets/kmader/pulmonary-chest-xray-abnormalities). In the proposed MedLedgerFL framework, raw medical images remain locally stored at participating hospitals and are not shared, in compliance with HIPAA and GDPR requirements. Only trained model parameters are exchanged during federated learning. All datasets were preprocessed using normalization, scaling, and augmentation, and split into training (70securely recorded on the blockchain.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, Telangana, 501203, India

    Dileep Kumar Murala

  2. Department of Computer Science and Engineering, Siddhartha Academy of Higher Education (Deemed to be University), Vijayawada, Andhra Pradesh, India

    Lavanya Vemulapalli

  3. Department of Emerging Technologies, Mahatma Gandhi Institute of Technology, Gandipet, Ranga Reddy, Hyderabad, 500075, Telangana, India

    Yadaiah Balagoni

  4. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India

    Eswar Patnala

  5. Department of Clinical Medicine and Community Health, University of Rwanda, P.O.Box 4285, Kigali, Rwanda

    Bananeza Romeo

Authors
  1. Dileep Kumar Murala
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  2. Lavanya Vemulapalli
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  3. Yadaiah Balagoni
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  4. Eswar Patnala
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  5. Bananeza Romeo
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Contributions

Dileep Kumar Murala: Conceptualization, Methodology, Software Implementation, Data Curation, Writing—Original Draft. Lavanya Vemulapalli: Formal Analysis, Validation, Visualization, Writing—Review & Editing. Yadaiah Balagoni: Supervision, Project Administration, Resources, Writing—Review & Editing. Eswar Patnala: Investigation, Experimental Design, Performance Evaluation, Writing—Review & Editing. Bananeza Romeo*: Conceptualization, Supervision, Critical Revision, Funding Acquisition, Writing—Review & Editing.

Corresponding author

Correspondence to Bananeza Romeo.

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Murala, D.K., Vemulapalli, L., Balagoni, Y. et al. MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39149-4

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  • Received: 23 August 2025

  • Accepted: 03 February 2026

  • Published: 11 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39149-4

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Keywords

  • Blockchain
  • Federated learning
  • Telemedicine
  • Privacy-preserving healthcare
  • Secure data sharing
  • Remote healthcare services
  • Decentralized trust
  • Predictive healthcare models
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