English(EN) Federated Nonparametric Regression with Heterogeneous Clients

联邦回归方法处理异构客户端数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了一种新颖的联邦非参数回归方法，特别是针对Nadaraya-Watson回归，旨在处理具有异构数据分布的客户端。该系统允许每个客户端将核摘要发送到中央服务器，然后中央服务器构建一个联邦估计器，而无需客户端的个体数据。这种方法使客户端能够将其本地预测与联邦模型相结合，通过减少平方预测损失来量化预测准确性的提高。 AI

影响为回归任务引入了一种新颖的联邦学习技术，有可能在数据多样的分布式环境中提高模型性能。

排序理由该集群包含一篇详细介绍机器学习新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Xiaoran Jiang, Anand N. Vidyashankar · 2026-10-26 04:00

具有异构客户端的联邦非参数回归

arXiv:2610.32178v1 Announce Type: new Abstract: Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty. We study this question for Nadaraya--Watson regression in a triangular array with potentially different…

本文如何被排名

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具有异构客户端的联邦非参数回归

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