An efficient training optimization method for federated learning in 6G networks

SU Rui; ZHU Yali; ZHU Xiaorong

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An efficient training optimization method for federated learning in 6G networks

更新时间：2026-03-03

- An efficient training optimization method for federated learning in 6G networks
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.江苏商贸职业学院，江苏南通 226000
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(92367102);The Science and Technology Planning Project of Nantong(MS2023062)
- DOI：
  CLC： TN929.5
- Received：03 December 2024，
  
  Revised：2025-01-10，
  
  Accepted：21 February 2025，
- 稿件说明：
移动端阅览
SU Rui, ZHU Yali, ZHU Xiaorong. An efficient training optimization method for federated learning in 6G networks[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

SU Rui, ZHU Yali, ZHU Xiaorong. An efficient training optimization method for federated learning in 6G networks[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

第六代移动通信技术(6G)预计将充分利用大量敏感数据并有望实现一个完全互联的世界，联邦学习作为一种新兴的分布式计算范式广泛应用于各领域。在传统的联邦学习方法中，收敛前的大量通信轮次导致了巨大的通信成本。此外，在每一轮训练中，客户端在上行通信阶段向参数服务器上传大量的模型参数，增加了通信开销。针对6G网络提出了一种高效的联邦学习训练优化方法(CEFED)，采用定制化的局部训练策略，通过约束项在更少的通信轮数内实现快速收敛。考虑到上行链路拥塞，引入了部分客户端参与规则，以避免多个客户端同时上传参数更新。同时针对数据异质性导致的局部模型之间的差异，提出了一种基于数据质量的加权聚合策略，通过为不同本地参数分配合理权值以提升模型精度。实验结果表明，该算法在多种联邦学习设置下，均超越了经典的FedAVG算法。与基准算法相比，CEFED算法不仅在提升测试精度的基础上加快了模型的收敛速度，而且有效节省了通信开销。

Abstract

The sixth generation of mobile communication technology (6G) is expected to make full use of a vast amount of sensitive data and is anticipated to achieve a completely interconnected world. Federated learning

as an emerging distributed computing paradigm

is widely applied in various fields. In traditional federated learning methods

a large number of communication rounds before convergence lead to significant communication costs. Moreover

during each training round

clients upload a large amount of model parameters to the parameter server during the uplink phase

increasing communication overhead. An efficient federated learning training optimization method (CEFED) has been proposed for 6G networks

which adopts a customized local training strategy to achieve rapid convergence within fewer communication rounds through a constraint term. Considering uplink congestion

a partial client participation rule is introduced to prevent multiple clients from uploading parameter updates simultaneously. At the same time

in response to the differences between local models caused by data heterogeneity

a weighted aggregation strategy based on data value is proposed

which assigns reasonable weights to different local parameters to improve model accuracy. Experimental results show that the algorithm outperforms the classic FedAVG algorithm in various federated learning settings. Compared with the benchmark algorithm

the CEFED algorithm not only accelerates the model's convergence rate while improving test accuracy

but also effectively saves on communication costs.

关键词

Keywords

references

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