Privacy-Preserving Diabetes Prediction Using Federated Learning in Edge-Based Healthcare

Privacy-Preserving Diabetes Prediction Using Federated Learning in Edge-Based Healthcare | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Privacy-Preserving Diabetes Prediction Using Federated Learning in Edge-Based Healthcare Gabriel Gomes de Oliveira, Suja A. Alex, J. Renees, Abdullah Ayub Khan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9182048/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 21 You are reading this latest preprint version Abstract The growing prevalence of diabetes highlights the need for scalable, accurate, and privacy-conscious testing technologies. To train models, traditional machine learning (ML) techniques often rely on centralised infrastructure that aggregates patient data from multiple sources onto a single server. Even though these methods frequently produce highly accurate predictions, they raise serious concerns about data privacy, legal compliance, and computational scalability. This article presents a Federated Learning (FL) framework for diabetes risk prediction, utilis-ing the PIMA Indian Diabetes dataset to overcome these issues. Without sending raw patient data, the suggested approach utilizes a supervised Deep Neural Network (DNN) that has been cooperatively trained across several decentralised clients, including wearable health devices and institutional medical servers. Each client independently trains the model on its local dataset, sharing only model parameters with a central server. The global model is updated through Federated Averaging (FedAvg), thereby safeguarding data privacy while preserving diagnostic accuracy. Experimental results validate the effectiveness of the proposed approach in balancing performance and privacy, demonstrating its potential for real-world deployment in edge-based smart healthcare systems. Federated Learning Edge Computing Decentralized Intelligence Deep Neural Network Diabetes Prediction Healthcare Full Text Additional Declarations No competing interests reported. Supplementary Files PrivacyPreservingDiabetesPredictionUsingFederatedLearninginEdgeBasedHealthcare.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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