F$^2$STNet is proposed, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA).
Abstract
Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence modeling and collaborative training. We propose F$^2$STNet, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA). The spectral branch exposes graph-frequency structure, while the state-space layer models long temporal dependencies with linear complexity in the sequence length. FFA adjusts the FedAvg prior using client validation losses and an increasing fairness schedule. Experiments on PeMS04, HZMetro, and KnowAir show favorable forecasting accuracy relative to the evaluated baselines; federated experiments on PeMS04 additionally improve worst-client and client-dispersion metrics.
Spatio-temporal graph neural networks achieve strong traffic forecasting accuracy, yet their robustness under out-of-distribution (OOD) conditions, such as traffic incidents, remains poorly understood. We propose SRGNet, a spectrally-regularized graph network combining three targeted innovations: (1) spectral normalization on all weight matrices to bound the Lipschitz constant; (2) disruption-aware training augmentation synthesizing incident-like flow drops; and (3) stochastic depth creating an implicit ensemble. We evaluate on PEMS-BAY using an impact-verified OOD protocol with 996 real incidents ( 30% flow reduction). SRGNet achieves the lowest OOD degradation (+116.0%) among competitive models, the best local OOD RMSE (0.987) at the most-impacted sensors, and a standard RMSE of 0.3123, demonstrating the best accuracy–robustness tradeoff.
Huu Dang Khoi Nguyen, T. Le· E3S Web of Conferences· 0 citations
FedTP is proposed, a federated learning framework that integrates gradient conflict elimination into the aggregation process and harmonizes local updates, thereby improving fairness across clients without compromising overall predictive accuracy.
Baobao Chai, Zhongyuan Yu, Tianqing He et al.· 0 citations
Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.
Zhi-Cheng Wang, Tao Zhang, Yi-Meng Zhu et al.· Applied Sciences· 0 citations
FedSTAR is proposed, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks and delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.
An improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations.
Jin Zhang, Feng-Min Tan, Wei Bai et al.· Italian National Conference...· 0 citations
This work proposes FedHAttn, a novel hierarchical attention–based aggregation mechanism that explicitly models inter-client model feature importance to optimize global model performance and establishes an effective aggregator that balances accuracy, robustness, and efficiency in federated PM2.5 prediction.
Sudhir Kumar, Vaneet Kour, Shivendu Mishra et al.· International Journal of Mac...· 0 citations