Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipeline outperforms baselines on various datasets, particularly in non-iid settings.

翻译：机器学习（ML）已彻底变革交通运输系统，实现了自动驾驶与智能交通服务。联邦学习（FL）通过在分布式系统中训练机器学习模型（仅交换模型参数而非原始数据），有效克服了隐私限制。然而，联网车辆的动态状态会影响网络连接质量，进而影响联邦学习性能。为应对这一挑战，我们提出一种情境化客户端选择流程：通过车联网（V2X）消息根据预测通信时延选择客户端。该流程包括：（i）融合车联网消息，（ii）预测未来交通拓扑，（iii）基于本地数据分布相似性对客户端进行预聚类，以及（iv）选择具有最小通信时延的客户端参与后续模型聚合。实验结果表明，本流程在多种数据集（尤其在非独立同分布场景下）上均优于基线方法。