EdgeFL-Crypto: Federated Split Learning Architecture for IoT- Based Cryptocurrency Volatility Prediction in Edge-Cloud Environments

EdgeFL-Crypto: Federated Split Learning Architecture for IoT- Based Cryptocurrency Volatility Prediction in Edge-Cloud Environments | 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 Article EdgeFL-Crypto: Federated Split Learning Architecture for IoT- Based Cryptocurrency Volatility Prediction in Edge-Cloud Environments Mohammed M. Alenazi¹, Abrar S. Alhazmi² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7873990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The proliferation of Internet of Things (IoT) devices in financial markets creates unprecedented opportunities for distributed intelligence in cryptocurrency trading systems. This paper presents EdgeFL-Crypto, a novel federated split learning architecture that leverages IoT sensor networks and edge-cloud computing paradigms for real-time cryptocurrency volatility forecasting. Our approach addresses the unique challenges of deploying transformer-based models across resource-constrained IoT devices by implementing a hierarchical federated learning protocol that splits model computation between edge and cloud layers. The framework integrates mobile edge computing nodes as intermediate aggregators, enabling efficient model training while preserving data locality in IoT ecosystems. We introduce an adaptive split point selection mechanism that dynamically partitions transformer layers based on device capabilities and network conditions, optimizing the trade-off between edge computation and cloud processing. The system employs blockchain-secured aggregation for trustworthy model updates across distributed IoT networks, ensuring integrity in multi-stakeholder environments. Experimental evaluation on real-world cryptocurrency data from IoT-enabled trading terminals demonstrates 23.7% improvement in prediction accuracy while reducing communication overhead by 41.2% compared to traditional cloud-centric approaches. The framework achieves sub-200ms inference latency suitable for real-time IoT applications, with differential privacy guarantees (ε=1.0) protecting sensitive trading data at the edge. Our results establish EdgeFL-Crypto as a practical solution for deploying sophisticated AI models in IoT-driven financial systems, bridging the gap between edge intelligence and cloud computing for next-generation fintech applications. Physical sciences/Engineering Physical sciences/Mathematics and computing federated split learning edge-cloud computing IoT financial systems distributed transformer networks mobile edge intelligence blockchain-secured aggregation real-time IoT analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Reviews received at journal 22 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 18 Oct, 2025 First submitted to journal 18 Oct, 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-7873990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538671448,"identity":"ad4d75ec-69d8-4086-b46b-556b095b96dd","order_by":0,"name":"Mohammed M. 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Environments","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"federated split learning, edge-cloud computing, IoT financial systems, distributed transformer networks, mobile edge intelligence, blockchain-secured aggregation, real-time IoT analytics","lastPublishedDoi":"10.21203/rs.3.rs-7873990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7873990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe proliferation of Internet of Things (IoT) devices in financial markets creates unprecedented opportunities for distributed intelligence in cryptocurrency trading systems. This paper presents EdgeFL-Crypto, a novel federated split learning architecture that leverages IoT sensor networks and edge-cloud computing paradigms for real-time cryptocurrency volatility forecasting. Our approach addresses the unique challenges of deploying transformer-based models across resource-constrained IoT devices by implementing a hierarchical federated learning protocol that splits model computation between edge and cloud layers. The framework integrates mobile edge computing nodes as intermediate aggregators, enabling efficient model training while preserving data locality in IoT ecosystems. We introduce an adaptive split point selection mechanism that dynamically partitions transformer layers based on device capabilities and network conditions, optimizing the trade-off between edge computation and cloud processing. The system employs blockchain-secured aggregation for trustworthy model updates across distributed IoT networks, ensuring integrity in multi-stakeholder environments. Experimental evaluation on real-world cryptocurrency data from IoT-enabled trading terminals demonstrates 23.7% improvement in prediction accuracy while reducing communication overhead by 41.2% compared to traditional cloud-centric approaches. The framework achieves sub-200ms inference latency suitable for real-time IoT applications, with differential privacy guarantees (ε=1.0) protecting sensitive trading data at the edge. Our results establish EdgeFL-Crypto as a practical solution for deploying sophisticated AI models in IoT-driven financial systems, bridging the gap between edge intelligence and cloud computing for next-generation fintech applications.\u003c/p\u003e","manuscriptTitle":"EdgeFL-Crypto: Federated Split Learning Architecture for IoT- Based Cryptocurrency Volatility Prediction in Edge-Cloud Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 15:15:44","doi":"10.21203/rs.3.rs-7873990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T07:38:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T01:29:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T16:13:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23809402499271721806551955853461133345","date":"2025-10-28T13:40:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91443317169736091263631867474720961638","date":"2025-10-28T11:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283938681667670215757828673429759714521","date":"2025-10-23T11:34:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T11:28:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T11:27:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T10:51:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T08:22:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-18T08:18:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db5f64d3-ae41-4b04-8a55-be662b2685a2","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57290166,"name":"Physical sciences/Engineering"},{"id":57290167,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-24T11:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 15:15:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7873990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7873990","identity":"rs-7873990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}