A Robust Federated Learning Method for Data Heterogeneity with Enhanced Momentum-guided Aggregation

A Robust Federated Learning Method for Data Heterogeneity with Enhanced Momentum-guided Aggregation | 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 A Robust Federated Learning Method for Data Heterogeneity with Enhanced Momentum-guided Aggregation Linhai Nie, Jin Wang, naixuan Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7978012/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Federated learning (FL) is an emerging distributed machine learning paradigm that enables multiple edge devices to collaboratively train a model for a specific task while preserving privacy. Yet, due to the non-independent and identically distributed (Non-IID) data dispersed on edge devices, FL suffers from slow convergence and low accuracy. This paper focuses on this problem, and proposes a robust method through enhanced data sharing. Specifically, this paper adopts feature distillation to obtain the performance sensitive features, which are used to generating proxy data for initializing public data before FL training. Meanwhile, We use a momentum-guided strategy for parameter aggregation. In order to evaluate the performance of the method, this paper also conducts many experiments. As demonstrated by the results, the method could outperform the state-of-the-art methods by 5.31% in terms of performance. Federated Learning Non-IID Data Feature Partitioning Gradient Momentum Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 29 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. 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