{"title":"NPD-SG: A Noise-Resistant Primal-Dual Stochastic Gradient Diffusion Algorithm Over Networks","authors":"Jiacheng Wu;Zhengchun Zhou;Sheng Zhang;Hongyu Han","doi":"10.1109/TSIPN.2025.3600760","DOIUrl":null,"url":null,"abstract":"In this paper, we develop a noise-resistant primal-dual stochastic gradient-based diffusion algorithm (named NPD-SG) designed to operate effectively in scenarios with link noise. The mean-square analysis indicates that, with enough small step-size <inline-formula><tex-math>$\\mu$</tex-math></inline-formula> and forgetting factor <inline-formula><tex-math>$\\gamma$</tex-math></inline-formula> in (0, 1), the strategy is stable in terms of mean-square error; by reducing the value of <inline-formula><tex-math>$\\gamma$</tex-math></inline-formula>, it is possible to maintain a low level of estimation error. Then, we modify the update step for dual variables to address the numerical accumulation problem, resulting in an improved NPD-SG (INPD-SG) algorithm. The theoretical analysis also reveals the impact of this modification on algorithm performance. Finally, several simulations demonstrate the theoretical findings and the effectiveness of the proposed approaches.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1087-1099"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11130908/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we develop a noise-resistant primal-dual stochastic gradient-based diffusion algorithm (named NPD-SG) designed to operate effectively in scenarios with link noise. The mean-square analysis indicates that, with enough small step-size $\mu$ and forgetting factor $\gamma$ in (0, 1), the strategy is stable in terms of mean-square error; by reducing the value of $\gamma$, it is possible to maintain a low level of estimation error. Then, we modify the update step for dual variables to address the numerical accumulation problem, resulting in an improved NPD-SG (INPD-SG) algorithm. The theoretical analysis also reveals the impact of this modification on algorithm performance. Finally, several simulations demonstrate the theoretical findings and the effectiveness of the proposed approaches.
期刊介绍:
The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.