F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
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Abstract (arXiv)
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 F2STNet, 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.
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References · 30
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18 more
- 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
- 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Modelcited by 24
- 2024MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendationcited by 21
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- 2023LightCTS: A Lightweight Framework for Correlated Time Series Forecastingcited by 18
- 2022Federated Learning for 5G Base Station Traffic Forecastingcited by 15
- 2023MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimizationcited by 13
- 2024AirShot: Efficient Few-Shot Detection for Autonomous Explorationcited by 10
- 2024Impact of Noisy Supervision in Foundation Model Learningcited by 8
- 2024Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approachcited by 8
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- 2024Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blockscited by 2
- 2025Hypercomplex Prompt-aware Multimodal Recommendationcited by 2
- 2025Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecastingcited by 1
- 2026Nested Spatio-Temporal Time Series Forecastingcited by 1
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