Federated Graph Learning:Problems,Methods and Challenges
Graph has been widely used in various fields for many years as an efficient,flexible,and versatile data structure.In re-cent years,graph-based deep learning algorithms have emerged,achieving significant success in areas like social network,bioinfor-matics,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.
万方数据
维普
