Computer Science ›› 2025, Vol. 52 ›› Issue (1): 362-373.doi: 10.11896/jsjkx.240500118

• Information Security • Previous Articles     Next Articles

Federated Graph Learning:Problems,Methods and Challenges

WANG Xin1,2, XIONG Shubo1, SUN Lingyun2   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Computer Science and Technology,Zhejiang University,Hangzhou 310058,China
  • Received:2024-05-26 Revised:2024-10-08 Online:2025-01-15 Published:2025-01-09
  • About author:WANG Xin,born in 1984,Ph.D,asso-ciate professor,master supervisor,is a member of CCF(No.11687M).His main research interests include machine learning,big data analysis and federated learning.
  • Supported by:
    Zhejiang University of Technology Science and Technology Project(KYY-HX-20220288,KYY-HX-20180649).

Abstract: Graph has been widely used in various fields for many years as an efficient,flexible,and versatile data structure.In recent years,graph-based deep learning algorithms have emerged,achieving significant success in areas like social network,bioinformatics,and recommendation systems.Although publicly graph data online is increasing,high-quality data remains scattered among different owners.With society’s growing demand for data privacy protection,existing graph learning algorithms require enhancement.Graph federated learning is a novel approach to addresses this issue.This paper systematically reviews the research progress in the field of federated graph learning over the past five years.The core problems in the field are divided into three parts,and the structure is vertically integrated and the relationships are progressively explained:1)structural heterogeneity from differences in raw graph data;2)model aggregation issues due to federated graph learning characteristics;3)overall model tuning.For each section,it provides a detailed analysis of representative works and their advantages and disadvantages,summarizes the typical applications and future challenges in the field of federated graph learning.

Key words: Federated learning, Graph neural network, Federated graph learning, Privacy computing

CLC Number: 

  • TP181
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