Security and privacy concerns in Federated Learning systems: a systematic review

Security and privacy concerns in Federated Learning systems: a systematic review | 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 Security and privacy concerns in Federated Learning systems: a systematic review Rosa Di Salvo, Antonino Galletta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7148932/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Federated Learning is a Machine Learning solution that trains a global model by aggregating weights from different peers. Federated Learning does not require that data be shared among nodes; however, it is not exempt from privacy and/or security issues. This systematic review focuses on the major security and privacy threats related to the definition and implementation of Federated Learning frameworks. This study aims to provide a comprehensive analysis of potential adversary cyber attacks throughout the execution of Federated Learning, in order to characterize and classify Federated Learning protocols capable of addressing critical robustness concerns — including privacy-preserving techniques, local data protection, efficiency, and accuracy — while highlighting the critical points that remain to be addressed. Federated Learning privacy protection data security cyber attacks homomorphic encryption differential privacy secret sharing blockchain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7148932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489881681,"identity":"cbca92c7-4982-4581-82bb-a792a3d3a3e1","order_by":0,"name":"Rosa Di Salvo","email":"","orcid":"","institution":"University of Messina","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"Di","lastName":"Salvo","suffix":""},{"id":489881682,"identity":"6b0248cc-0696-4565-b6f3-7952e5dea37d","order_by":1,"name":"Antonino Galletta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3RsUrEMBjA8UghLsGs3yGkr/BJ4FzEZ0ko3NShkziIFAQnwVVx9Alu6fxJwS59gMrd0OPgJodbhUNNtfSWi66C+Q9JC/mlKWEsFPqz4de4R2037eeMGTdz7/JoIIwMQ2CCeuI10fbxm4DpXz1EPtw+LbNsrph8bMmcby7l/eu4XaQn7CDOdxKYl5G+w5VmsEIyNQLM0mO0xcR/sCbhhwJLmwMh2Wt3sFk6BluUXhJvSbUm+44Qv9Qd+fASHIi8cV/JEbARHSEvOWoSrR3RHERG5lmPpvXkzJFEcG52EtXYxVJsSiVlNW3XF0qqqixGb8Wpiq/I8/99fLiOPvHz+i75y56hUCj0f/sE101ZkK0L+GsAAAAASUVORK5CYII=","orcid":"","institution":"University of Messina","correspondingAuthor":true,"prefix":"","firstName":"Antonino","middleName":"","lastName":"Galletta","suffix":""}],"badges":[],"createdAt":"2025-07-17 12:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7148932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7148932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89078233,"identity":"e56d0b10-aab8-42bf-a160-058357213edb","added_by":"auto","created_at":"2025-08-14 12:32:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1037341,"visible":true,"origin":"","legend":"","description":"","filename":"SecurityandprivacyissuesinFederatedLearningsystemsnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7148932/v1_covered_a336554e-28ec-44d2-a3ff-fd1606c152d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Security and privacy concerns in Federated Learning systems: a systematic review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Federated Learning, privacy protection, data security, cyber attacks, homomorphic encryption, differential privacy, secret sharing, blockchain","lastPublishedDoi":"10.21203/rs.3.rs-7148932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7148932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Federated Learning is a Machine Learning solution that trains a global model by aggregating weights from different peers. Federated Learning does not require that data be shared among nodes; however, it is not exempt from privacy and/or security issues. This systematic review focuses on the major security and privacy threats related to the definition and implementation of Federated Learning frameworks. This study aims to provide a comprehensive analysis of potential adversary cyber attacks throughout the execution of Federated Learning, in order to characterize and classify Federated Learning protocols capable of addressing critical robustness concerns — including privacy-preserving techniques, local data protection, efficiency, and accuracy — while highlighting the critical points that remain to be addressed.","manuscriptTitle":"Security and privacy concerns in Federated Learning systems: a systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 18:05:33","doi":"10.21203/rs.3.rs-7148932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eab2d30d-fd16-4479-9d52-0d0f61dd59c2","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T12:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 18:05:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7148932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7148932","identity":"rs-7148932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}