FedeRICo：面向交通流预测的联邦区域影响耦合

中文摘要 AI 辅助

城市交通预测通常依赖于分布在多个利益相关方之间的信息，这些利益相关方可能因隐私或商业限制而无法共享原始数据，这推动了联邦时空方法的发展。在这种联邦设置中，每个客户端在具有自身空间拓扑和时间动态的独特传感器子图上观察交通，导致客户端之间存在显著异质性。现有的联邦时空方法通常依赖模型参数聚合，并且为恢复跨客户端边界的空间依赖性提供的机制有限。这引入了两个关键限制。具体而言，跨异构图域的参数聚合往往会稀释客户端特定的表示，而路网划分则破坏了跨客户端边界的交通动态传播。为了解决这些挑战，我们提出了FedeRICo，一种联邦交通预测框架，它结合了梯度级协作与边界感知的残差通信。FedeRICo采用双分支预测架构，其中全局引导分支捕获可迁移的预测结构，而私有残差分支保留客户端特定的修正并整合边界残差信号。全局分支通过所有客户端的梯度对齐进行协调，从而实现协作优化而不会产生破坏性的参数干扰。为了恢复跨客户端的空间依赖性，通过趋势-残差分解提取边界消息，该分解抑制周期性结构，并仅在物理相邻客户端之间传递瞬态时空残差信号。在四个真实世界交通预测基准上的实验表明，FedeRICo始终优于最先进的联邦时空基线，同时保持有竞争力的训练运行时间。

英文摘要

Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial constraints, motivating federated spatial-temporal approaches. In such federated settings, each client observes traffic over a distinct sensor subgraph with its own spatial topology and temporal dynamics, leading to significant heterogeneity across clients. Existing federated spatial-temporal methods typically rely on model parameter aggregation and provide limited mechanisms for recovering spatial dependencies across client boundaries. This introduces two key limitations. Specifically, parameter aggregation across heterogeneous graph domains tends to dilute client-specific representations, while road network partitioning breaks the propagation of traffic dynamics across client boundaries. To address these challenges, we propose FedeRICo, a federated traffic forecasting framework that combines gradient-level collaboration with boundary-aware residual communication. FedeRICo employs a dual-branch forecasting architecture in which a globally guided branch captures transferable forecasting structure, while a private residual branch preserves client-specific corrections and incorporates boundary residual signals. The global branch is coordinated through gradient alignment across all clients, enabling collaborative optimisation without destructive parameter interference. To recover cross-client spatial dependencies, boundary messages are extracted through a trend-residual decomposition that suppresses periodic structure and communicates only transient spatial-temporal residual signals between physically adjacent clients. Experiments across four real-world traffic forecasting benchmarks demonstrate that FedeRICo consistently outperforms state-of-the-art federated spatial-temporal baselines while maintaining competitive training runtime.

发表机构

• University of Exeter（埃克塞特大学）

机构由 AI 辅助整理，请以论文原文为准。