FedGDAN:通过联合图形传播注意力网络进行隐私保护的流量预测
Yuanhui Li1, Bo Mi2, Ran Zeng3
1Chongqing Jiaotong University, School of Traffic and Transportation, Chongqing, 400074, PR China.
Scientific reports
|November 20, 2025
概括
联合图形扩散注意网络 (FedGDAN) 通过实现协作流量预测来增强智能交通系统. 这种方法可以提高准确性和隐私性,而无需共享原始车辆数据.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 高效的数据利用和隐私保护是智能运输系统 (ITS) 的关键挑战,特别是众多智能连接车辆 (ICV).
- 传统的机器学习模型在分布式环境中与复杂的时空依赖性和数据隐私作斗争.
研究的目的:
- 提出FedGDAN,一个新的联邦图形扩散注意力网络,用于ITS中的协作流量预测.
- 解决传统方法在捕捉时空依赖性方面的局限性,同时确保数据隐私和局部性.
主要方法:
- 将图形神经网络 (GNN) 与联合学习 (FL) 结合起来,以便在不共享原始数据的情况下进行协作预测.
- 在道路网络中建模全球时空相关性.
- 实施适应性局部聚合机制来处理非独立且相同分布的数据.
主要成果:
- 与最先进的集中式和联合式方法相比,FedGDAN表现优越.
- 在流量预测准确度方面取得了显著的改进,平均绝对误差增加了3%-10%.
- 在复杂的ITS环境中有效维护数据隐私和局部性.
结论:
- FedGDAN为智能连接车辆的交通流量预测提供了强大而准确的解决方案.
- 提出的方法有效地平衡了协作学习与严格的隐私要求.
- 强调了将GNN和FL集成到先进的ITS应用程序中的潜力.
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