Efficient Multi-Modal Fusion via Preconditioned and Asynchronously Parallel ADMM for On-Device Personalization and Security

Efficient Multi-Modal Fusion via Preconditioned and Asynchronously Parallel ADMM for On-Device Personalization and Security | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 March 2026 V1 Latest version Share on Efficient Multi-Modal Fusion via Preconditioned and Asynchronously Parallel ADMM for On-Device Personalization and Security Authors : Li Wei Ming 0009-0004-3442-8416 [email protected] and Aarav Sharma Authors Info & Affiliations https://doi.org/10.22541/au.177247871.19351740/v1 103 views 60 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The convergence of multi-modal sensing and constrained computing on personal devices demands efficient algorithmic frameworks for fusing heterogeneous data streams-visual, depth, inertial, and biometric-under stringent latency, memory, and power constraints. This paper presents a unified mathematical framework for on-device multi-modal fusion, grounded in constrained convex optimization and operator splitting theory. We formulate the fusion problem as minimization of a composite objective function, coupling modality-specific loss terms through linear consensus constraints, regularized by structured sparsity and low-rank priors. The central contribution is a Preconditioned Asynchronously Parallel Alternating Direction Method of Multipliers (PAP-ADMM), tailored for architectures with heterogeneous computational loads across modalities. We derive closed-form solutions for proximal operators associated with logistic regression, group lasso, and nuclear norm regularization, which are ubiquitous in personalization and security tasks. Convergence analysis establishes a non-asymptotic rate of O(1/k) under bounded delay conditions. Extensive experiments on synthetic benchmarks and a concrete application-fusing RGB and depth features for contactless palm-print authentication-validate the framework's efficacy. The proposed solver achieves up to 3.5x speedup over synchronous ADMM and reduces memory footprint by 45% on embedded hardware, while maintaining or improving accuracy. This work provides both theoretical foundations and practical tools for developing efficient, private, and robust on-device intelligent systems. Supplementary Material File (efficient_multi_modal_fusion.pdf) Download 525.09 KB Information & Authors Information Version history V1 Version 1 02 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords admm biometrics convex optimization edge computing multi-modal fusion Authors Affiliations Li Wei Ming 0009-0004-3442-8416 [email protected] View all articles by this author Aarav Sharma Nippon Institute of Technology View all articles by this author Metrics & Citations Metrics Article Usage 103 views 60 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Li Wei Ming, Aarav Sharma. Efficient Multi-Modal Fusion via Preconditioned and Asynchronously Parallel ADMM for On-Device Personalization and Security. Authorea . 02 March 2026. DOI: https://doi.org/10.22541/au.177247871.19351740/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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