  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.
