AI Lab | 协会大数据专委会讲座：基于激励机制的公平图联邦学习研究

SUFE AI Lab讲座

【时间地点】

主办方：帜讯-上财联合实验室师生党支部 & 上海开源信息技术协会大数据专委会

时间：2023年11月13日（周一）10:30--11:30

地点：信管学院102报告厅

【讲座介绍】

题目：Towards Fair Graph Federated Learning via Incentive Mechanisms

摘要：Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This stands in contrast to previous FL incentive mechanisms that assume all agents contribute positively and in a timely manner. In view of this, this paper presents a novel incentive mechanism tailored for fair graph federated learning, integrating incentives derived from both model gradient and payoff. To achieve this, we first introduce an agent valuation function aimed at quantifying agent contributions through the introduction of two criteria: gradient alignment and graph diversity. Moreover, due to the high heterogeneity in graph federated learning, striking a balance between accuracy and fairness becomes particularly crucial. We introduce motif prototypes to enhance accuracy, communicated between the server and agents, enhancing global model aggregation and aiding agents in local model optimization. Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.

【主讲人介绍】

许嘉蓉，复旦大学青年副研究员。主要研究方向包括图数据挖掘，隐私计算。曾在NeurIPS、KDD、AAAI、IJCAI、EMNLP、WSDM、TKDE、TKDD等学术会议和期刊发表论文20余篇，获人工智能顶会AAAI 2023杰出论文奖。担任中国中文信息学会社会媒体处理专委会委员，中国中文信息学会青年工作委员会，担任KDD、NeurIPS、ICLR、WWW、AAAI、WSDM、EMNLP、TKDE等多个重要国际会议程序委员会委员及期刊评审。