Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity

arXiv:2609.29600 · cs.LG · Submitted 2026-08-27 · Read on arXiv

cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Federated Learning — 29× in this paper · explained in 15 episodes
• Robustness — 12× in this paper · explained in 5 episodes
• Generalization — 10× in this paper · explained in 13 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Aleatoric Uncertainty — 7× in this paper · explained in 5 episodes
• Active Learning — 6× in this paper · explained in 6 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Data Heterogeneity — 6× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Agent Interactions — 4× in this paper · explained in 1 episode
• Non-IID Data — 4× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Miss Rate — 3× in this paper
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Conference on Robot Learning — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• International Conference on Learning Representations — 2× in this paper
• International Conference on Machine Learning — 2× in this paper
• Selection Bias — 2× in this paper · explained in 4 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Aleatoric Uncertainty (AU) — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Federated Learning Based Trajectory Prediction — 1× in this paper
• GNNs — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• International Conference on Communications — 1× in this paper
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
• Scene Transformer: A unified architecture for predicting multiple agent trajectories
• Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction
• New examples of W$^*$ and C$^*$-superrigid groups
• Variance Reduction in SGD by Distributed Importance Sampling

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks