有效的联合图形聚合用于维护隐私的基于GNN的会话建议
Jing Lou1, Cheng Rong2, Hanshen Chen2
1College of Intelligent Transportation, Zhejiang Institute of Communications, Hangzhou, China. loujing@zjvtit.edu.cn.
Scientific reports
|July 3, 2025
概括
联合图形聚合 (FedGA) 通过在联合学习 (FL) 中有效地合并本地模型来增强保护隐私的建议. 这种方法克服了基于会话的建议与非IID数据的挑战,实现了最先进的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 图形神经网络 (GNN) 在推系统中表现出色,但在隐私的联邦学习 (FL) 中面临挑战.
- FL约束阻止了全局图形构建,非IID会话数据降低了模型性能.
- 从稀疏的局部图表中合并本地模型在保护隐私的场景中是低效的.
研究的目的:
- 引入一种新的自适应联合学习方法,即联合图形聚合 (FedGA),用于保护隐私的基于会话的建议.
- 在基于FL的GNN建议中解决分散图形构建和非IID数据的挑战.
- 开发一个高效的聚合器,用于合并在局部图形嵌入上训练的本地模型.
主要方法:
- 介绍了联邦图汇总 (FedGA),这是一个自适应的FL方法,结合了阻差聚合 (DRA) 和条件第二时刻估计 (C-SME).
- 开发了一种高效的聚合器,用于合并在未见的本地图嵌入上训练的本地模型.
- 在极端非IIDness下,结合了优化模型的策略,而不会受到激进学习率的干扰.
主要成果:
- 即使在极端的非IID数据条件下,FedGA也有效地优化模型.
- 理论分析表明,FedGA实现了与其他自适应FL方法相比的收率.
- 在开放和现实数据集上的经验验证证明了与现有的自适应FL方法相比,最先进的性能.
结论:
- FedGA成功地弥合了基于FL和GNN的会议建议之间的差距.
- 拟议的方法实现了最先进的性能,同时保持了与集中式方法可比的结果.
- 在联邦设置中,FedGA为保护隐私的GNN建议提供了高效和有效的解决方案.
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