Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence

Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence | 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 Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence Nurahmed Ali Yassin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9491795/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 (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, conventional federated learning architectures remain susceptible to a broad spectrum of security and privacy threats, including model poisoning, gradient inversion, inference attacks, and Byzantine faults. This paper presents a unified and robust framework— Secure and Privacy-Preserving Federated AI (SPFA) — that integrates differential privacy, homomorphic encryption, secure multi-party computation, Byzantine fault tolerance, and zero-knowledge proofs into a unified, production-grade architecture. We formally analyze the threat model, prove privacy guarantees under the ( ε , δ )-differential privacy framework, and demonstrate Byzantine resilience under partial adversarial participation. Extensive experiments on heterogeneous data distributions across image classification, natural language processing, and medical diagnosis benchmarks demonstrate that SPFA achieves model accuracy within 2.3% of centralized baselines while providing provable ε = 1.0 privacy with a communication overhead of only 18% above standard FedAvg. To the best of our knowledge, our framework is among the first to consolidate all five protection layers into a unified, deployable system with formal analysis and an open-source reference implementation. The relevance of SPFA extends to privacy-sensitive applications in healthcare, cybersecurity, distributed edge computing, and smart city analytics. federated learning differential privacy homomorphic encryption secure multi-party computation Byzantine fault tolerance zero-knowledge proofs distributed machine learning privacy-preserving AI adversarial robustness gradient privacy 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-9491795","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627497317,"identity":"3e58d509-3c0d-4bbe-bb32-d6ad4b304276","order_by":0,"name":"Nurahmed Ali Yassin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFACxgYQKQciDjwgRYsxWEsCKXYlgjUSpcWcvbl1c2HbvfT5YYcfAm2xk9NtIKDFsudg2+2ZbcW5G2+nGQC1JBubHSCgxeBGYttt3raE3I2zE0BaDiRuI6jl/kOwlnTD2ekfiNRygxGsJUFeOodYW84AHcZzLsFwg3ROwYEEA2L8cvz4s9s8ZQny8rPTN3/4UGEnR1ALQi9YpQGxykFAvoEU1aNgFIyCUTCiAACAHUj1St73FQAAAABJRU5ErkJggg==","orcid":"","institution":"Adama University","correspondingAuthor":true,"prefix":"","firstName":"Nurahmed","middleName":"Ali","lastName":"Yassin","suffix":""}],"badges":[],"createdAt":"2026-04-22 06:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9491795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9491795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978284,"identity":"2d5c94b8-6cd3-4d7e-9ab4-f66b2e843269","added_by":"auto","created_at":"2026-05-11 11:35:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":656551,"visible":true,"origin":"","legend":"","description":"","filename":"FederatedAICybersecurityYassin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9491795/v1_covered_1fff186b-6fc5-40fe-bea7-7f563c7e5077.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, differential privacy, homomorphic encryption, secure multi-party computation, Byzantine fault tolerance, zero-knowledge proofs, distributed machine learning, privacy-preserving AI, adversarial robustness, gradient privacy","lastPublishedDoi":"10.21203/rs.3.rs-9491795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9491795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFederated learning (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, conventional federated learning architectures remain susceptible to a broad spectrum of security and privacy threats, including model poisoning, gradient inversion, inference attacks, and Byzantine faults. This paper presents a unified and robust framework\u0026mdash; Secure and Privacy-Preserving Federated AI (SPFA) \u0026mdash; that integrates differential privacy, homomorphic encryption, secure multi-party computation, Byzantine fault tolerance, and zero-knowledge proofs into a unified, production-grade architecture. We formally analyze the threat model, prove privacy guarantees under the (\u003cem\u003eε\u003c/em\u003e, \u003cem\u003eδ\u003c/em\u003e)-differential privacy framework, and demonstrate Byzantine resilience under partial adversarial participation. Extensive experiments on heterogeneous data distributions across image classification, natural language processing, and medical diagnosis benchmarks demonstrate that SPFA achieves model accuracy within 2.3% of centralized baselines while providing provable \u003cem\u003eε\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0 privacy with a communication overhead of only 18% above standard FedAvg. To the best of our knowledge, our framework is among the first to consolidate all five protection layers into a unified, deployable system with formal analysis and an open-source reference implementation. 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