AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns, most existing methods rely on local training, resulting in data silos and limited knowledge sharing. Federated Learning (FL) enables privacy-preserving collaborative training, but standard FL struggles with non-independent and identically distributed (non-IID) data across clients. Personalized Federated Learning (PFL) has emerged as a promising paradigm, yet current PFL frameworks require extensive adaptation for traffic prediction tasks, such as graph feature engineering and network architecture design. Moreover, many prior methods depend on dataset-specific hyper-parameter optimization, which impedes practical deployment. To address these challenges, we propose AutoFed, a novel PFL framework for traffic prediction that significantly reduces reliance on manual hyper-parameter tuning and feature design. Inspired by prompt learning, AutoFed introduces a federated representor that employs a client-aligned adapter to distill local data into a compact, globally shared prompt matrix. This prompt then conditions a personalized predictor, allowing each client to benefit from cross-client knowledge while maintaining local specificity. Extensive experiments on real-world datasets demonstrate that AutoFed consistently achieves superior performance across diverse scenarios. The code of this paper is provided at anonymous repository https://anonymous.4open.science/r/AutoFed-3440 .