{"paper_id":"31e618a7-896f-47d0-a5b4-8a29540a757e","body_text":"Federated Gradient Boosting using Minimal Variance Sampling | 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 Federated Gradient Boosting using Minimal Variance Sampling William Lindskog-Münzing, Daniel Nata Nugraha, Christian Prehofer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4730699/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Federated learning (FL) has emerged as a paradigm for training machine learning models on decentralized data while preserving privacy. This paper introduces a novel approach called Federated gradient BOOsting using Minimal variance sampling (FedBOOM). FedBOOM leverages gradient boosting to construct a robust global model by iteratively aggregating weak learners trained on locally sampled data from clients. Crucially, it employs minimal variance sampling (MVS) to select relevant data points from each client, reducing overhead communication and mitigating the impact of data heterogeneity. Through theoretical analysis and extensive experiments, we find that FedBOOM achieves a superior performance with a subsampling fraction of less than 1.0. In most experiments, a fraction of 0.4-0.6 is optimal and outperforms that of no subsampling (fraction of 1.0) with on average 5.5%. Use of optimal subsampling fraction for FedBOOM can also reduce communication cost with 40% on average. Federated Learning Minimal Variance Sampling Gradient Boosting Decision Tree XGBoost Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Sep, 2024 Reviews received at journal 24 Aug, 2024 Reviews received at journal 18 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers invited by journal 06 Aug, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 14 Jul, 2024 First submitted to journal 12 Jul, 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. 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