English(EN) Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks

联邦学习策略可缩短工业物联网的训练时间和能源消耗

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员为在工业物联网网络上运行的联邦学习系统开发了一种新的带宽分配策略。该策略将参与设备划分为有序子集，依次授予每个子集独占访问全部带宽的权限。该方法旨在最大限度地减少总训练时间并减少上行链路能耗，这对于电池受限的设备尤其有利。 AI

影响这种新颖的带宽分配策略可以提高联邦学习在工业物联网环境中的效率，从而缩短连接设备的训练时间和能耗。

排序理由详细介绍新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Kangmin Kim, Jaeyoung Song · 2026-06-01 04:00

面向工业物联网网络的联邦学习的带设备分区的带宽分配

arXiv:2605.30892v1 Announce Type: new Abstract: We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless channels without sharing local data. In such systems, communication time is a prim…