AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)
Xin Chang, Lixin Liu, Jingyu Wang, Jinling Yu, Xiaolin Zhang
Abstract
Federated Graph Learning enables multiple clients to collaboratively train graph models while protecting local private data. However, most studies have assumed that all clients contribute data voluntarily and actively. Without reasonable incentives, clients are often reluctant to contribute personal data for model training. Furthermore, the budget for incentives is limited, and if clients with low-quality graph data are incentivized to participate in training, it will negatively impact the training performance of all parties in the system. To address this, we propose AEFGL, a Reverse Auction and Value Evaluation-Based Incentive Mechanism for Federated Graph Learning. First, we design a reverse auction mechanism combining graph structural attribute motifs with client production value. Then, we propose a method for evaluating client production value based on the comparison of the client
BibTeX
@inproceedings{aaai2026_aefglreverseauct,
title = {AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)},
author = {Xin Chang and Lixin Liu and Jingyu Wang and Jinling Yu and Xiaolin Zhang},
booktitle = {AAAI 2026},
year = {2026}
}