推荐系统
计算机科学
水准点(测量)
联合学习
弱势群体
人工神经网络
数据建模
人工智能
图形
样品(材料)
理论(学习稳定性)
训练集
数据挖掘
机器学习
理论计算机科学
数据库
化学
大地测量学
色谱法
政治学
法学
地理
作者
Zheng Li,Muhammad Bilal,Xiaolong Xu,Jielin Jiang,Yan Cui
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:19 (1): 673-682
标识
DOI:10.1109/tii.2022.3203395
摘要
Recommender systems are technology-driven marketing solutions for businesses that analyze user behavior data. However, collaborative data sharing between enterprises is often prohibited by privacy protection regulations, leading to insufficient data for graph neural networks (GNNs) training. Fortunately, federated learning (FL), a collaborative training framework without exposing source data, can be applied congruently. Nevertheless, most of FL-based GNN model training methods adopt federated averaging, which performs poorly on highly heterogeneous graph data. To solve this problem, a FL-based GNN Model Training framework for cross-enterprise recommendation, named FL-GMT, is proposed. Specifically, a GNN-based recommendation model is deployed as the local training model. Then, considering the performance inequity caused by uneven sample quality, a loss-based federated aggregation algorithm is designed, effectively improving the performance of disadvantaged participants. To improve the system stability at the end of the aggregation, a dynamic update method of loss attention is designed. Extensive experiments on benchmark datasets demonstrate that FL-GMT outperforms baselines in terms of system fairness, stability, and accuracy.
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