Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity
Read the original on arXiv Machine Learning →The paper introduces active client selection strategies for federated learning in autonomous vehicle trajectory prediction, addressing challenges of high scene uncertainty and heterogeneous complexity across different driving environments. It proposes uncertainty-aware selectors that use per-client negative log-likelihood and aleatoric uncertainty, as well as a joint selector that balances scene complexity and uncertainty to prioritize informative clients. Experiments on the Argoverse dataset show that federated models outperform local training, with uncertainty-aware selection speeding convergence and improving key metrics, while the joint selector yields the best generalization under strong heterogeneity.
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