用LSTM提升时序建模，结合客户端验证实现隐私保护的动态时空预测
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
- 用LSTM替代GRU，更好捕捉长期时间依赖
- 客户端验证机制使更新参数更有效，准确率显著提升
- 适合需要隐私保护的交通、城市规划等实时预测场景
本文提出一种先进的联邦学习框架，用于复杂时空数据预测，优于现有最先进模型。将先前动态时空图卷积循环网络（DSTGCRN）中的门控循环单元（GRU）模块替换为长短期记忆（LSTM）网络，使模型更有效地捕捉时间序列中的长期依赖关系，显著提升对多样化预测任务中复杂时间模式的建模能力。此外，所提联邦学习框架引入客户端验证（CSV）机制，在客户端层面加入关键验证步骤，确保仅保留最有效的服务器参数更新，从而增强模型在各客户端上的鲁棒性与准确性。通过在真实世界应用中的大量实验验证，包括多模式交通需求预测公开数据集和城市出行起讫（OD）矩阵的私有数据集，结果表明该方法相比传统方法有显著改进，充分展现其在保留数据隐私的同时捕捉复杂时空依赖的能力。本工作不仅为实时、区域定制化的预测与管理提供可扩展、隐私保护的解决方案，也凸显了在联邦学习框架下利用分布式数据源的巨大潜力。算法已开源至GitHub。
原文摘要 · Abstract (English)
This paper presents an advanced Federated Learning (FL) framework for forecasting complex spatiotemporal data, improving upon recent state-of-the-art models. In the proposed approach, the original Gated Recurrent Unit (GRU) module within previous Dynamic Spatial--Temporal Graph Convolutional Recurrent Network (DSTGCRN) modeling is first replaced with a Long Short-Term Memory (LSTM) network, enabling the resulting model to more effectively capture long-term dependencies inherent to time series data. The resulting architecture significantly improves the model's capacity to handle complex temporal patterns in diverse forecasting applications. Furthermore, the proposed FL framework integrates a novel Client-Side Validation (CSV) mechanism, introducing a critical validation step at the client level before incorporating aggregated parameters from the central server into local models, ensuring only the most effective updates are retained and improving both the robustness and accuracy of the forecasting model across clients. The efficiency of our approach is demonstrated through extensive experiments on real-world applications, including public datasets for multimodal transport demand forecasting and private datasets for Origin-Destination (OD) matrix forecasting in urban areas. The results demonstrate substantial improvements over conventional methods, highlighting the framework's ability to capture complex spatiotemporal dependencies while preserving data privacy. This work not only provides a scalable and privacy-preserving solution for real-time, region-specific forecasting and management but also underscores the potential of leveraging distributed data sources in a FL context. We provide our algorithms as open-source on GitHub
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