{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T20:21:45Z","timestamp":1771618905202,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wuxi Science and Technology Development Fund Project","award":["K20241048"],"award-info":[{"award-number":["K20241048"]}]},{"name":"Wuxi Science and Technology Development Fund Project","award":["K20241029"],"award-info":[{"award-number":["K20241029"]}]},{"name":"Wuxi University Research Start-up Fund for Introduced Talents","award":["2023r020"],"award-info":[{"award-number":["2023r020"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JUSRP202501036"],"award-info":[{"award-number":["JUSRP202501036"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Federated learning enables distributed model training across edge nodes without direct raw data sharing, but model parameter transmission still poses significant privacy risks. To address this vulnerability, a Distributed Logistic Regression Gaussian Perturbation (DLGP) algorithm is proposed, which integrates the Alternating Direction Method of Multipliers (ADMM) with a calibrated differential privacy mechanism. The centralized logistic regression problem is decomposed into local subproblems that are solved independently on edge nodes, where only perturbed model parameters are shared with a central server. The Gaussian noise injection mechanism is designed to optimize the privacy\u2013utility trade-off by introducing calibrated uncertainty into parameter updates, effectively obscuring sensitive information while preserving essential model characteristics. The \u21132-sensitivity of local updates is derived, and a rigorous (\u03f5,\u03b4)-differential privacy guarantee is provided. Evaluations are conducted on a real-world dataset, and it is demonstrated that DLGP maintains favorable performance across varying privacy budgets, numbers of nodes, and penalty parameters.<\/jats:p>","DOI":"10.3390\/e27111148","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T09:10:45Z","timestamp":1763025045000},"page":"1148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated Logistic Regression with Enhanced Privacy: A Dynamic Gaussian Perturbation Approach via ADMM from an Information-Theoretic Perspective"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5888-7802","authenticated-orcid":false,"given":"Jie","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Automation, Wuxi University, Wuxi 214122, China"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, Wuxi University, Wuxi 214122, China"}]},{"given":"Hao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"}]},{"given":"Wentao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3136","DOI":"10.1109\/TNNLS.2023.3338867","article-title":"Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging","volume":"36","author":"Yang","year":"2025","journal-title":"IEEE Trans. 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