most citedFederated Learning for Traffic Flow Prediction with Synthetic Data Augmentation

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.AI2026

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

Fermin Orozco, Man Luo, Johan Wahlström

Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial constraints, motivating feder…

cs.LG2024★ 2 cited

Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation

Fermin Orozco, Pedro Porto Buarque de Gusmão, Hongkai Wen +2

Deep-learning based traffic prediction models require vast amounts of data to learn embedded spatial and temporal dependencies. The inherent privacy and commercial sensitivity of s…