EDGE-CLIENT COLLABORATIVE FEDERATED GRAPH LEARNING WITH ADAPTIVE NEIGHBOR GENERATION

Abstract: an edge-client collaborative federated graph learning with adaptive neighbor generation is provided. to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. in spreadfgl, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. next, a new negative sampling mechanism is developed to make spreadfgl concentrate on more refined information in downstream tasks. to facilitate load balancing at the edge layer, spreadfgl follows a distributed training manner that enables fast model convergence. using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed spreadfgl.

Inventor(s): Zheyi CHEN, Luying ZHONG, Yifan HUANG, Longxiang XUE

CPC Classification: G06N3/08 (Learning methods)

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