Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to data-scarce cities without traffic data exchange, existing approaches in Federated Traffic Knowledge Transfer (FTT) still face several critical challenges such as potential privacy leakage, cross-city data distribution discrepancies, and low data quality, hindering their practical application in real-world scenarios. To this end, we present FedTT, a novel privacy-aware and efficient federated learning framework for cross-city traffic knowledge transfer. Specifically, our proposed framework includes three key innovations: (i) a traffic view imputation method for missing traffic data completion to enhance data quality, (ii) a traffic domain adapter for uniform traffic data transformation to address data distribution discrepancies, and (iii) a traffic secret aggregation protocol for secure traffic data aggregation to safeguard data privacy. Extensive experiments on 4 real-world datasets demonstrate that the proposed FedTT framework outperforms the 14 state-of-the-art baselines.

翻译：交通预测旨在利用历史交通数据预测未来交通状况，在城市计算和交通管理中发挥着关键作用。尽管迁移学习和联邦学习已被用于通过将交通知识从数据丰富的城市迁移到数据稀缺的城市（无需交换交通数据）来解决交通数据稀缺问题，但联邦交通知识迁移（FTT）中的现有方法仍面临若干关键挑战，例如潜在的隐私泄露、跨城市数据分布差异以及数据质量低下，阻碍了其在实际场景中的应用。为此，我们提出了FedTT，一种新颖的、具有隐私意识且高效的联邦学习框架，用于跨城市交通知识迁移。具体而言，我们提出的框架包含三项关键创新：（i）一种用于补全缺失交通数据的交通视图插补方法，以提升数据质量；（ii）一种用于统一交通数据转换的交通领域适配器，以解决数据分布差异问题；以及（iii）一种用于安全交通数据聚合的交通秘密聚合协议，以保障数据隐私。在4个真实世界数据集上进行的大量实验表明，所提出的FedTT框架优于14个最先进的基线方法。