Adaptive Federated Bucketized Decision Tree Algorithm for Industrial Digital Twins
SUN Shibao
ZHAO Yifan
ZHAO Pengcheng
LIU Jianfeng
LI Xin
Abstract：Accurate modeling between physical devices and digital twins in industrial digital twins requires synchronizing massive amounts of sensing data.This results in difficulty ensuring real-time virtual-physical mapping.To address this issue,this paper proposes a four-layer bidirectional closed-loop architecture suitable for digital twins,consisting of a device,federated,cloud,and application layers.We develop a distributed federated bucketized decision tree algorithm utilizing histogram-based gain for global aggregation based on this architecture.Additionally,we design a pruning algorithm with adaptive gradient-weight redistribution to accelerate model convergence.Experimental results on the dataset demonstrate that the proposed federated aggregation model achieves accuracy improvements of 10％,11％,and 22％compared to baseline methods.It also achieves convergence improvements of 15％,20％,and 27％.Moreover,the proposed model maintains additional communication overhead within 300 mJ,even under the worst channel conditions.
Keywords：industrial internet of thingsdigital twinfederated learningbucketized decision tree
Publication Date：2025-08-25
Online Publishing Date：2025-09-16(First online date of this platform, not the publication date of the document)
Pages：12( 53-64 )