Research Paper#Federated Learning, Traffic Prediction, Prompt Learning, AI🔬 ResearchAnalyzed: Jan 3, 2026 06:29
AutoFed: Automated Federated Traffic Prediction
Published:Dec 31, 2025 04:52
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of traffic prediction in a privacy-preserving manner using Federated Learning. It tackles the limitations of standard FL and PFL, particularly the need for manual hyperparameter tuning, which hinders real-world deployment. The proposed AutoFed framework leverages prompt learning to create a client-aligned adapter and a globally shared prompt matrix, enabling knowledge sharing while maintaining local specificity. The paper's significance lies in its potential to improve traffic prediction accuracy without compromising data privacy and its focus on practical deployment by eliminating manual tuning.
Key Takeaways
- •Proposes AutoFed, a novel Personalized Federated Learning (PFL) framework for traffic prediction.
- •Eliminates the need for manual hyper-parameter tuning, improving practicality.
- •Employs prompt learning with a client-aligned adapter and a globally shared prompt matrix.
- •Achieves superior performance on real-world datasets.
Reference
“AutoFed consistently achieves superior performance across diverse scenarios.”