FedGMH：基于全局多头部的标签干扰消除方法研究

孙圳峰; 倪郑威

doi:10.11959/j.issn.1000-0801.2026005

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FedGMH：基于全局多头部的标签干扰消除方法研究

研究与开发 | 更新时间：2026-03-02

- FedGMH：基于全局多头部的标签干扰消除方法研究
- FedGMH: research on label interference elimination via global multi-head
- 电信科学 2026年42卷第1期页码：159-173
- 作者机构：
  
  浙江工商大学信息与电子工程学院，浙江杭州 310018
- 作者简介：
  
  [ "孙圳峰（2000- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为联邦学习。" ]
  [ "倪郑威（1989- ），男，博士，浙江工商大学信息与电子工程学院副研究员、硕士生导师，主要研究方向为人工智能、无线网络。" ]
- 基金信息：
  
  桐乡市通用人工智能研究院项目(TAGI2-B-2024-0017)
- DOI：10.11959/j.issn.1000-0801.2026005
  中图分类号： TN918;TP309
- 收稿：2025-03-09，
  
  修回：2025-07-10，
  
  录用：2025-07-28，
  
  纸质出版：2026-01-20
- 稿件说明：
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孙圳峰,倪郑威.FedGMH：基于全局多头部的标签干扰消除方法研究[J].电信科学,2026,42(01):159-173.

Sun Zhenfeng,Ni Zhengwei.FedGMH: research on label interference elimination via global multi-head[J].Telecommunications Science,2026,42(01):159-173.
孙圳峰,倪郑威.FedGMH：基于全局多头部的标签干扰消除方法研究[J].电信科学,2026,42(01):159-173. DOI： 10.11959/j.issn.1000-0801.2026005.

Sun Zhenfeng,Ni Zhengwei.FedGMH: research on label interference elimination via global multi-head[J].Telecommunications Science,2026,42(01):159-173. DOI： 10.11959/j.issn.1000-0801.2026005.

摘要

个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾，忽视了全局信息中的不同标签信息带来的干扰，尤其在维护单一全局头部的算法中，容易出现标签间特征冲突导致的收敛困难。为此，提出一种新的算法——全局多头部联邦学习（federated learning with global multi-head，FedGMH）算法，该算法在服务器创建多个全局头部，每个头部专门处理一种标签信息，而客户端下载与本地标签相关的全局头部，从而避免无关标签信息的干扰。此外，FedGMH引入参数级聚合机制：评估头部参数重要性，并将关键参数更新为全局多头部的加权参数，以加快收敛速度并且提高准确率。在3个视觉数据集上的大量实验表明，FedGMH优于现有的先进算法。

Abstract

Personalized federated learning has garnered significant attention due to its advantages in addressing data heterogeneity and privacy protection. However

existing algorithms predominantly focus on balancing the contradiction between global and personalized information

often overlooking the interference caused by distinct label information within global features. Particularly in algorithms maintaining a single global head

conflicts between label-specific features can lead to convergence challenges. To address this

a novel algorithm—federated learning with global multi-head (FedGMH) was proposed. The proposed algorithm creates multiple global heads on the server

each dedicated to processing one category of label information. Clients selectively download global heads relevant to their local labels

thereby avoiding interference from unrelated label information. Furthermore

FedGMH incorporates a parameter-level aggregation mechanism: it assesses the importance of head parameters and updates critical parameters to a weighted ensemble of the global multi-head

accelerating convergence and improving accuracy. Extensive experiments on three visual datasets demonstrate that FedGMH outperforms state-of-the-art baseline algorithms.

关键词

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references

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