{"paper_id":"26a224d9-5e23-4fef-b38b-65994be5f90d","body_text":"Reputation System based on Distributed Ledge to Secure DecentralizedFederated Learning | 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 Reputation System based on Distributed Ledge to Secure DecentralizedFederated Learning Jan von der Assen, Sandrin Raphael Hunkeler, Alberto Huertas Celdran, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4997851/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Machine Learning (ML) faces several challenges, including susceptibility to data leakage and the overhead associated with data storage. Decentralized Federated Learning (DFL) offers a robust solution to these issues by eliminating the need for centralized data collection, thereby enhancing data privacy. In DFL, distributed nodes collaboratively train an ML model by sharing model parameters rather than sensitive data. However, DFL systems are vulnerable to poisoning attacks, where malicious participants manipulate their local models or training data to compromise the overall model. Existing robust aggregation methods attempt to mitigate these threats by evaluating the quality of models based on specific criteria before and during aggregation. However, these methods rely solely on the local perspectives of individual DFL participants, limiting their effectiveness in identifying malicious actors. More specifically, the role of Distributed Ledger technology in providing a reputation-based aggregation approach for decentralized learning has not been explored. Moreover, experiments with reputation-based attacks have not been performed. Thus, this work introduces a ledger-based reputation system that enables participants to share their local reputation assessments, which are then combined into a reputation score. This score informs a robust aggregation algorithm, facilitating weighted aggregation. Experimental results demonstrate that the proposed system effectively mitigates model poisoning attacks and defenses against attacks targeting the reputation system itself. Additionally, resource utilization metrics reveal trade-offs and scalability limitations, with the reputation system providing valuable information to participants while maintaining competitive latency levels. Decentralized Federated Learning Reputation Secure Aggregation Distributed Ledger Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviews received at journal 25 Oct, 2024 Reviewers agreed at journal 05 Oct, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers invited by journal 29 Sep, 2024 Editor assigned by journal 04 Sep, 2024 Submission checks completed at journal 03 Sep, 2024 First submitted to journal 29 Aug, 2024 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. 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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-4997851\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":360256809,\"identity\":\"e126be27-d405-4058-84cb-9acb7f7a6180\",\"order_by\":0,\"name\":\"Jan von der 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