Distributed Convolutional Rank Regression over Decentralized Networks
Chunjing Li Tiange Zhao Xiaohui Yuan
作者信息
Abstract
This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework, in which estimators are obtained by solving consensus-constrained optimization with kernel-smoothed rank loss. The developed estimation scheme relies solely on local node data and information shared by neighboring nodes, thereby achieving privacy preservation and high communication efficiency. For heterogeneous network settings, we establish finite-sample error bounds for the decentralized CRR estimator and derive exact support recovery guarantees for the sparse decentralized CRR Lasso estimator. To facilitate numerical implementation, we adopt a generalized consensus ADMM to efficiently solve local subproblems across all network nodes. We verify the favorable performance of our developed approach via extensive numerical simulations and real-data experiments.引用本文复制引用
Chunjing Li,Tiange Zhao,Xiaohui Yuan.Distributed Convolutional Rank Regression over Decentralized Networks[EB/OL].(2026-07-28)[2026-07-31].https://arxiv.org/abs/2607.23639.学科分类
计算技术、计算机技术
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