交通预测旨在利用历史交通数据预测未来交通状况，在城市计算与交通管理中发挥关键作用。尽管迁移学习与联邦学习已被用于通过将交通知识从数据丰富城市迁移到数据匮乏城市（无需交换交通数据）来缓解交通数据稀缺问题，但现有联邦交通知识迁移（FTT）方法仍面临若干关键挑战，包括潜在的隐私泄露、跨城市数据分布差异以及低数据质量，从而制约其在真实场景中的实际应用。为此，我们提出 FedTT——一种新颖的、注重隐私保护且高效的跨城市联邦学习交通知识迁移框架。具体而言，该框架包含三项关键技术贡献：（i）一种交通视图插补方法，用于填补缺失的交通数据以提升数据质量；（ii）一种交通域适配器，实现交通数据的统一转换以应对数据分布差异；（iii）一种交通密文聚合协议，支持安全的交通数据聚合以保障数据隐私。在4个真实世界数据集上的大量实验表明，所提出的 FedTT 框架性能优于14种当前最先进的基线方法。

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.