F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

• URL: http://arxiv.org/abs/2608.09082v1
• Date: Mon, 10 Aug 2026 03:29:15 GMT
• Title: F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
• Abstract summary: F$2$STNet is a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, and graph convolution.<n>Experiments on PeMS04, HZMetro, and KnowAir show favorable forecasting accuracy relative to the evaluated baselines.
• Score: 14.982972903182896
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting [83.06074370551887]
  A fixed context window is mismatched to the time-varying optimal look-back of non-stationary price processes.<n>We propose a Mixture-of-Experts framework designed to adaptively determine the temporal context for each input sample.<n> Experiments on cumulative log-return prediction (HS300, S&P500) and fund sales forecasting demonstrate that RAVEN achieves SOTA performances, improves Pearson correlation by 9.2% on HS300 and 20.2% on S&P500, and reduces MSE by 18.2% on fund sales forecasting.
  arXiv  Detail & Related papers  (2026-06-23T02:11:04Z)
• INDEQS: Informed Neural controlled Differential EQuationS [13.60302952986747]
  We introduce Informed Informed Neural controlled Differential EQuationS (INDEQS), a graph-based NCDE forecasting method.<n>INDEQS separates inner mixing of hidden states across graph nodes from outer mixing between vector field and control.<n>We evaluate INDEQS on two real-world tasks: river discharge forecasting on a hydrological network and traffic flow prediction on PeMS08.
  arXiv  Detail & Related papers  (2026-06-17T14:46:23Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts [49.9321870703948]
  Existing models predominantly focus on short-horizon predictions and suffer from notorious computational costs and memory consumption.<n>We present FaST, an effective and efficient framework based on Mixture-of-Experts (MoEs) for long-horizon and large-scale STG forecasting.<n>FaST is underpinned by two key innovations. First, an adaptive graph agent attention mechanism is proposed to alleviate the computational burden.<n>Second, we propose a new parallel MoE module that replaces traditional feed-forward networks with Gated Linear Units (GLUs)
  arXiv  Detail & Related papers  (2026-01-08T18:00:58Z)
• S$^2$Transformer: Scalable Structured Transformers for Global Station Weather Forecasting [67.93713728260646]
  Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting.<n>This contradicts the nature underlying observations of the global weather system limiting forecast performance.<n>We propose a novel Structured Spatial Attention in this paper.<n>It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph.<n>It aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention -- considering both spatial proximity and global correlation.
  arXiv  Detail & Related papers  (2025-09-10T05:33:28Z)
• Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks [7.944991952472549]
  We propose a Transformer-based data prediction method that integrates global spatial semantics and temporal behavior.<n>We show that our model outperforms existing state-of-the-art prediction models in shortterm and long-term predictions.
  arXiv  Detail & Related papers  (2025-08-25T10:22:03Z)
• Temporal Graph MLP Mixer for Spatio-Temporal Forecasting [1.5696662871407674]
  Temporal Graph-Mixer (T-GMM) architecture designed to address missing data challenges.<n>Model combines node-level processing with subgraph encoding to capture localized spatial dependencies.<n>Experiments on AQI, ENGRAD, PV-US and METR-LA datasets demonstrate model's ability to effectively forecast even in the presence of significant missing data.
  arXiv  Detail & Related papers  (2025-01-17T14:13:48Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective [48.00240550685946]
  Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. We propose a novel Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. Our experiments on seven datasets have demonstrated superior performance with higher efficiency and fewer parameters compared with state-of-the-
  arXiv  Detail & Related papers  (2023-11-10T17:13:26Z)
• Graph Federated Learning for CIoT Devices in Smart Home Applications [23.216140264163535]
  We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed G-Fedfilt'' The proposed aggregator enables a structured flow of information based on the graph's topology. It is capable of yielding up to $2.41%$ higher accuracy than FedAvg in the case of testing the generalization of the models.
  arXiv  Detail & Related papers  (2022-12-29T17:57:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.