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 surrounding traffic data, most existing methods rely on local training, resulting in data silos and limited knowledge sharing. Federated Learning (FL) offers an efficient solution through privacy-preserving collaborative training; however, standard FL struggles with the non-independent and identically distributed (non-IID) problem among clients. This challenge has led to the emergence of Personalized Federated Learning (PFL) as a promising paradigm. Nevertheless, current PFL frameworks require further adaptation for traffic prediction tasks, such as specialized graph feature engineering, data processing, and network architecture design. A notable limitation of many prior studies is their reliance on hyper-parameter optimization across datasets-information that is often unavailable in real-world scenarios-thus impeding practical deployment. To address this challenge, we propose AutoFed, a novel PFL framework for traffic prediction that eliminates the need for manual hyper-parameter tuning. 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 https://github.com/RS2002/AutoFed .

翻译：精确的交通预测对于智能交通系统至关重要，包括网约车服务、城市道路规划及车队管理。然而，由于交通数据涉及重大隐私关切，现有方法大多依赖本地训练，导致数据孤岛和知识共享受限。联邦学习通过隐私保护的协同训练提供了高效解决方案；然而，标准联邦学习在处理客户端间非独立同分布问题时面临困难。这一挑战促使个性化联邦学习成为前景广阔的研究范式。尽管如此，现有个性化联邦学习框架仍需针对交通预测任务进行进一步适配，例如专门的图特征工程、数据处理和网络架构设计。先前研究的显著局限在于其依赖跨数据集的超参数优化——此类信息在实际场景中往往难以获取——从而阻碍了实际部署。为应对这一挑战，我们提出AutoFed，一种无需人工超参数调优的新型个性化联邦学习交通预测框架。受提示学习启发，AutoFed引入联邦表征器，通过客户端对齐适配器将本地数据蒸馏为紧凑的全局共享提示矩阵。该提示进而对个性化预测器进行条件约束，使各客户端在保持本地特异性的同时能够受益于跨客户端知识。基于真实数据集的广泛实验表明，AutoFed在多样化场景中均能持续取得优越性能。本文代码发布于https://github.com/RS2002/AutoFed。