Privacy-Preserving Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management

Privacy-Preserving Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management | 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 Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management Munusamy S, Jothi K R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7709911/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The rapid integration of machine learning in healthcare emphasizes the need for privacy-preserving and efficient solutions, especially when managing sensitive Electronic Health Records (EHRs). Existing federated learning (FL) frameworks face significant challenges, including high communication overhead, computational inefficiency on resource-constrained edge devices, limited privacy guarantees during inference, and vulnerability to noisy or malicious updates. This study proposes a novel Privacy-Preserving Edge Intelligence Framework (PPEIF), designed specifically to overcome these limitations by combining Homomorphic Encryption (HE), Knowledge Distillation (KD), and an Attention-Based Aggregation Mechanism. In the PPEIF framework, a large teacher model trains lightweight student models via knowledge distillation, enabling efficient and encrypted inference directly at edge nodes. Homomorphic Encryption ensures that raw EHR data remains encrypted throughout local processing, preventing any data leakage. Instead of transmitting full model parameters, only encrypted distilled logits are shared, drastically reducing communication overhead. The attention mechanism dynamically weighs local contributions during global aggregation, mitigating the effects of noisy or malicious updates. Experimental evaluation on the MIMIC-III dataset demonstrates that PPEIF achieves 94.5% inference accuracy, significantly lower inference time (1.9 seconds), and up to 80% reduction in communication overhead compared to conventional FL methods. Privacy leakage risk is minimized, achieving the highest privacy level by protecting both model updates and inference results. Comparative analysis with state-of-the-art works further validates the superiority of PPEIF in terms of accuracy, scalability (supporting over 200 edge nodes), and real-time deployment feasibility. The proposed framework offers a robust and practical solution for secure, efficient, and scalable healthcare applications, setting a new benchmark for future privacy-preserving federated learning research in sensitive domains. Blockchain Federated Learning Edge Analytics Electronic Health Records Privacy-Preserving Secure Healthcare Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 04 Jan, 2026 Reviewers invited by journal 03 Jan, 2026 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 27 Sep, 2025 First submitted to journal 25 Sep, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7709911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594364055,"identity":"51165e6e-9026-4457-9429-b475a6d0d243","order_by":0,"name":"Munusamy S","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Munusamy","middleName":"","lastName":"S","suffix":""},{"id":594364056,"identity":"76ea08fa-0b70-4637-8bad-fb28f9dca056","order_by":1,"name":"Jothi K R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxgYGNoYEIOQHMUGAjbAWZogWyQbGxgaitDAwMIPUJDAYHIBZQ1BDe/+xBw8Y0vKNbyS3P2CosWPgkyagk7HnMLtBAkOO5bYbiUCHHUtmYJM5QEDLjGQ2iQSGCgOzMweBWtgOMIC4xGkx7gFp+Ue8lhwDA/bGxgbGNmK09Bw2k0gwSDOQON7YOCOxL5mHoBbD9sZnkj8qkg34m9kffPjwzU5OfgYhLQ0g0gDKAyrmwa8eCOQJqhgFo2AUjIJRAACgjjv1AHsQ+AAAAABJRU5ErkJggg==","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jothi","middleName":"K","lastName":"R","suffix":""}],"badges":[],"createdAt":"2025-09-25 07:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7709911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7709911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105754129,"identity":"ad7e29df-211f-4036-8120-ff79cd29664f","added_by":"auto","created_at":"2026-03-30 16:14:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":686296,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPT22092025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7709911/v1_covered_00db961c-c5ac-4d9d-af3a-acb685cc361f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Privacy-Preserving Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cloud-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clco","sideBox":"Learn more about [Journal of Cloud Computing](http://journalofcloudcomputing.springeropen.com)","snPcode":"13677","submissionUrl":"https://submission.nature.com/new-submission/13677/3","title":"Journal of Cloud Computing","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blockchain, Federated Learning, Edge Analytics, Electronic Health Records, Privacy-Preserving, Secure Healthcare Systems","lastPublishedDoi":"10.21203/rs.3.rs-7709911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7709911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid integration of machine learning in healthcare emphasizes the need for privacy-preserving and efficient solutions, especially when managing sensitive Electronic Health Records (EHRs). Existing federated learning (FL) frameworks face significant challenges, including high communication overhead, computational inefficiency on resource-constrained edge devices, limited privacy guarantees during inference, and vulnerability to noisy or malicious updates. This study proposes a novel Privacy-Preserving Edge Intelligence Framework (PPEIF), designed specifically to overcome these limitations by combining Homomorphic Encryption (HE), Knowledge Distillation (KD), and an Attention-Based Aggregation Mechanism. In the PPEIF framework, a large teacher model trains lightweight student models via knowledge distillation, enabling efficient and encrypted inference directly at edge nodes. Homomorphic Encryption ensures that raw EHR data remains encrypted throughout local processing, preventing any data leakage. Instead of transmitting full model parameters, only encrypted distilled logits are shared, drastically reducing communication overhead. The attention mechanism dynamically weighs local contributions during global aggregation, mitigating the effects of noisy or malicious updates. Experimental evaluation on the MIMIC-III dataset demonstrates that PPEIF achieves 94.5% inference accuracy, significantly lower inference time (1.9 seconds), and up to 80% reduction in communication overhead compared to conventional FL methods. Privacy leakage risk is minimized, achieving the highest privacy level by protecting both model updates and inference results. Comparative analysis with state-of-the-art works further validates the superiority of PPEIF in terms of accuracy, scalability (supporting over 200 edge nodes), and real-time deployment feasibility. The proposed framework offers a robust and practical solution for secure, efficient, and scalable healthcare applications, setting a new benchmark for future privacy-preserving federated learning research in sensitive domains.\u003c/p\u003e","manuscriptTitle":"Privacy-Preserving Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 16:00:40","doi":"10.21203/rs.3.rs-7709911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-23T20:50:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160717572772144366293537403283646338577","date":"2026-01-05T07:03:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290116272473883508845952833373188859136","date":"2026-01-04T18:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-03T17:56:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T05:32:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T18:05:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cloud Computing","date":"2025-09-25T07:18:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cloud-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clco","sideBox":"Learn more about [Journal of Cloud Computing](http://journalofcloudcomputing.springeropen.com)","snPcode":"13677","submissionUrl":"https://submission.nature.com/new-submission/13677/3","title":"Journal of Cloud Computing","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb572f48-87b0-4999-8710-9798509fa8e4","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:00:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 16:00:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7709911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7709911","identity":"rs-7709911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}