Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting

2025-03-06

AI Summary

This paper addresses weak long-range temporal dependency modeling and poor model robustness in complex spatiotemporal forecasting under privacy-sensitive settings. To this end, we propose a novel federated learning framework tailored for spatiotemporal prediction. Methodologically: (1) LSTM replaces GRU to enhance long-term temporal dependency capture; (2) dynamic spatiotemporal graph convolution is integrated to model multi-scale spatial correlations; (3) a client-side validation (CSV) mechanism—first introduced in this work—filters low-quality local updates, thereby improving global model convergence stability. Extensive experiments on heterogeneous, multi-source traffic data—including origin-destination matrices and multimodal demand signals—demonstrate that our framework achieves significantly higher prediction accuracy and robustness than state-of-the-art baselines, while strictly preserving data privacy. The implementation code is publicly available.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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. This ensures that only the most effective updates are adopted, 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.

Problem

• Improves spatiotemporal data forecasting using Federated Learning.
• Enhances model accuracy with Client-Side Validation mechanism.
• Preserves data privacy while handling complex temporal patterns.

Innovation

• Replaces GRU with LSTM for better time series capture
• Introduces Client-Side Validation for robust updates
• Demonstrates scalability and privacy in spatiotemporal forecasting