Additive-Effect Assisted Learning

• 成果类型: Article
• 署名作者: Zhang, Jiawei; Yang, Yuhong; Ding, Jie
• 署名单位: University of Kentucky; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkaf062
• 发表日期: 2026-04
• 页码: 657-676
• 关键词: additive effects; assisted learning; Decentralized learning; Generalized Linear Model; Hypothesis Testing; coordinate descent method; linear-regression; CONVERGENCE; algorithm

摘要

It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modelling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: first, learners may need to keep data values or even variable names undisclosed due to, e.g. commercial interest or privacy regulations; second, there are restrictions on the number of transmission rounds between them due to e.g. communication costs. To address these challenges, we develop a two-stage assisted learning architecture for an agent, Alice, to seek assistance from another agent, Bob. In the first stage, we propose a privacy-aware hypothesis testing-based screening method for Alice to decide on the usefulness of the data from Bob, in a way that only requires Bob to transmit sketchy data. Once Alice recognizes Bob's usefulness, Alice and Bob move to the second stage, where they jointly apply a synergistic iterative model training procedure. With limited transmissions of summary statistics, we show that Alice can achieve the oracle performance as if the training were from centralized data, both theoretically and numerically.